This is a follow-up to my previous article, “What Is Ethnography,” outlining ways ethnography is useful in professional settings.
To recap, I defined ethnography as a research approach that seeks “to understand the lived experiences of a particular culture, setting, group, or other context by some combination of being with those in that context (also called participant-observation), interviewing or talking with them, and analyzing what is produced in that context.”
Ethnography is a powerful tool, developed by anthropologists and other social scientists over the course of several decades. Here are three types of situations in professional settings when I have found to use ethnography to be especially powerful:
1. To see the given product and/or people in action
2. When brainstorming about a design
3. To understand how people navigate complex, patchwork processes
Situation
#1: To See the Given Product and/or People in Action
Ethnography allows you to witness people in action: using your product or service, engaging in the type of activity you are interested, or in whatever other situation you are interested in studying.
Many other social science research methods involve creating an artificial environment in which to observe how participants act or think in. Focus groups, for example, involve assembling potential customers or users into a room: forming a synthetic space to discuss the product or service in question, and in many experimental settings, researchers create a simulated environment to control for and analyze the variables or factors they are interested in.
Ethnography, on the other hand, centers around observing and understanding how people navigate real-world settings. Through it, you can get a sense for how people conduct the activity for which you are designing a product or service and/or how people actually use your product or service.
For example, if you want to understand how people use GPS apps to get around, one can see how people use the app “in the wild:” when rushing through heavy traffic to get to a meeting or while lost in the middle of who knows where. Instead of hearing their processed thoughts in a focus group setting or trying to simulate the environment, you can witness what the tumultuousness yourself and develop a sense for how to build a product that helps people in those exact situations.
Situation
#2: When Brainstorming about a New Product Design
Ethnography is especially useful during the early stages of designing a product or service, or during a major redesign. Ethnography helps you scope out the needs of your potential customers and how they approach meeting said needs. Thus, it helps you determine how to build a product or service that addresses those needs in a way that would make sense for your users.
During such initial stages of product design, ethnography helps determine the questions you should be asking. Many have a tendency during these initial stages to construct designs based on their own perception of people’s needs and desires and miss what the customers’ or users’ do in fact need and desire. Through ethnography, you ground your strategy in the customers’ mindsets and experiences themselves.
The brainstorming stages of product development also require a lot of flexibility and adaptability: As one determines what the product or service should become, one must be open to multiple potential avenues. Ethnography is a powerful tool for navigating such ambiguity. It centers you on the users, their experiences and mindsets, and the context which they might use the product or service, providing tools to ask open-ended questions and to generate new and helpful ideas for what to build.
Situation
#3: To Understand How People Navigate Complex, Patchwork Processes
At a past company, I analyzed how customer service representatives regularly used the various software systems when talking with customers. Over the years, the company had designed and bought various software programs, each to perform a set of functions and with unique abilities, limitations, and quirks. Overtime, this created a complex web of interlocking apps, databases, and interfaces, which customer service representatives had to navigate when performing their job of monitoring customer’s accounts. Other employees described the whole scene as the “Wild West:” each customer service representative had to create their own way to use these software systems while on the phone with a (in many cases disgruntled) customer.
Many companies end up building such patchwork systems – whether of software, of departments or teams, of physical infrastructure, or something else entirely – built by stacking several iterations of development overtime until, they become a hydra of complexity that employees must figure out how to navigate to get their work done.
Ethnography is a powerful tool for making sense of such processes. Instead of relying on official policies for how to conduct various actions and procedures, ethnography helps you understand and make sense of the unofficial and informal strategies people use to do what they need. Through this, you can get a sense for how the patchwork system really works. This is necessary for developing ways to improve or build open such patchwork processes.
In the customer service research project, my task was
to develop strategies to improve the technology customer service representatives
used as they talked with customers. Seeing how representatives used the
software through ethnographic research helped me understand and focus the analysis
on their day-to-day needs and struggles.
Conclusion
Ethnography is a powerful tool, and the business world and other professional settings have been increasingly realizing this (c.f. this and this ). I have provided three circumstances where I have personally found ethnography to be invaluable. Ethnography allows you to experience what is happening on the ground and through that to shape and inform the research questions we ask and recommendations or products we build for people in those contexts.
Interviewing for a data science role can be a daunting task, especially for those new to the field. I have lost count of the number of data science interviews I have had over the years, but here are the four most common questions I have encountered and strategies for preparing for each. Prepping for these questions is a great opportunity to develop your story thesis, the most important part of any data science interview.
Most Common Data Science Questions:
1) Tell me about yourself.
2) Describe a data science job you have worked on.
3) What kind of experience do you have with messy data?
4) What programming languages and software have you used?
Question 1: Tell me about yourself.
This is probably hands down the most common interview question across all industries and fields, not just data science, so the fact that it is the most commonly asked questions in data science interviews may not seem that surprising. A good answer is crucial to establish a favorable first impression and to lay your main story or thesis of who you are that you will come back to throughout the interview.
In data science interviews, I emphasize my passion for using data science tools to help organizations solve complex problems that were previously vexing. If you are unsure what your thesis is, I designed this activity to help people decipher it. Here is an example of how I would describe myself:
“I fell in love with data science because I enjoy helping organizations solve complex problems. In my past roles, I have used my combined data science and social science skills to explore and build solutions for complicated problems for which the typical ways of doing things within the organization have not worked. I am energized by the intellectual stimulation of breaking down complex problems and using data science to develop potential innovative yet useful solutions. What kind of problems do you guys have that has led you to need to find a data scientist like me?”
Your self-description should tell the story of who you are in a way that demonstrates how you would be a natural fit for the role and helpful to the organization. As your interview thesis, if you laid it out well, then every other question you answer will simply involve fleshing out one (or a combination) of those three basic parts of your self-story: 1) Who you are, 2) How your identity makes you a natural fit for the role, and 3) How this would benefit the organization.
Here are four other important observations to note about how I told my story:
I emphasized who I was – an innovator developing unique solutions to complex problems – while showing my innovator identity naturally connects with data science and could be helpful for the organization. You might not consider yourself an “innovator” per se, but the trick is to figure out who you are based on what energizes and impassions you and then show how performing the data science role you are applying for is a natural fit for who you are.
I told the story with normal words, not technical jargon. I have found that many, if not most, of my interviews, especially the first-round interviews, are with employees without technical expertise, and since you often do not know the level of technical expertise of the interviewer, it is better to err on the non-technical side.
I kept my story positive, only mentioning what I like to do. Sometimes people instinctively try to illustrate what they want by describing things they do not like to do: e.g. “At previous last job, I learned I do not like doing Y, so I am seeking to do X instead” or “I am doing Y, and I hate it. I want out.” I would describe these aspects of my story later if the interviewer asks, but I would stick with the positive at first: only mentioning what I want to do.
I used strong, subjective, even emotional phrases like “fell in love with,” “passionate about,” and “energized by.” At first glance, these phrases might seem overly informal, but I have found they help interviewers remember me. Do not overdo it, but being more vivid and personable is generally helps rather than hurts your interview chances for data science positions.
Question 2: Describe a data science project you have worked on.
This is the second most common question I encountered, so make sure you come prepared with an exemplar project to showcase. They may ask you a lot of questions about your project, so I would recommend choosing a project where you did an amazing job on, really knocked it out of the park and that you are proud of. Unless there are disclosure issues, post your work on GitHub, a blog, LinkedIn, or somewhere else online, and include a link to it in your job application.
How to explain the project will vary considerably depending on your interviewer’s degree of expertise. I generally start with a non-technical, high level explanation and provide the technical details if the interviewer(s) prompts me to with follow-up questions. This gives the interviewers the freedom to choose the level of technical expertise they would like in their follow-up. A data scientist interviewer worth his or her salt will quickly steer the conversation into more technical aspects of your project that he or she wants to learn more about, but even then, starting non-technical demonstrates that you know how to effectively communicate your work to non-technical audiences as well.
When describing your project, you are effectively telling the story of the project, and most project stories have the following core components:
Who: You are probably the story’s protagonist (it is your interview after all, so naturally pick a project or part of a project where you were the primary driver), but there are likely multiple important side characters that you will need to setup, like who commissioned the project, who it was for, who the data was about, and so on.
What: The problem, need, or question your project sought to address generally forms the “conflict” of the project story, so be sure to explain what led to the problem, need, or question (in stories, called the inciting incident).
When and Where: The timeframe setting/context in which the project took place (e.g., the organization you were working with or a class you took for which the project was for). How long you had to complete the project can also be important to establish.
How: What did you to solve the problem. If you tried a lot of approaches before discovering what works, the how includes both your methodological story and your final solution (that is part of the rising and falling action for how you overcame the project). This is the meat of your story. You will want technical and non-technical descriptions of the how:
Technical How: Generally, the core two parts of a technical description are the model you used (and any you tried if applicable) and how you determined the features/variables you selected. Another important part might be how you cleaned and/or gleaned the data.
Non-technical How: I have found that non-technical audiences usually do not glean much from either the model I ended using or my feature selection procedure. Instead, I explain what type of functionality I ensured the model had to solve the problem I had just setup: for example, “I built a model that calculated the probability of X phenomena based on data sources A, B, and C, testing various types of models to determine which would do this best, and then discerned which variables among those datasets were the best to use.” For a non-technical audience, that is generally enough. The core component for them is what goes into the model (the data), what result the model produced from it, and how that informed the problem, need, or question driving the project.
Finally, in your how explanation, make sure you slip in whatever programming languages and software you used: Python, R, SQL, Azure, etc.
Why: This is your explanation of why you chose the approach(es) you did for your how. Now, just like with the how, you will need a technical and non-technical explanations of the why.
Make sure your non-technical explanation of why aligns with your non-technical how. I commonly see data scientists make the mistake of going over a non-technical individual’s head by trying to provide a technical why explanation for their non-technical how. In particular, I would not explain the metric or criteria you used to compare models or decide the feature selection procedure in my non-technical explanation, since these will likely lose a non-technical person. If my non-technical how description focused what data the model used and what it did with it, then my non-technical why focuses on why building a model to do that mattered and how it helped others and/or myself in the real world.
What happened: This is the result of the project. Did you succeed or fail (or somewhere in between)? Was it useful for whoever you built it for? Were you able to conduct any follow-up analysis after deployment? Maybe most importantly, what did you learn from the experience? In narrative terminology, this is the resolution. The more you can quantitatively measure any outcomes the better.
These are the basic components of a project story. Here is the most common project I use, and when reading through it, feel free to analyze how I present each component of the story. I wrote this blog for a general audience, so I provided my non-technical how and why.
Question 3: What kind of experience do you have with messy data?
Interviewers ask me this question surprisingly frequently. They usually preface the question by explaining that they at the organization have a lot of messy data that would require cleaning/processing for their future data scientist. This is a great opportunity to showcase your comfort with data science and data science issues.
I typically answering something like this:
“Yes, I have had to organize and clean messy data all the time. That’s par for the course in data science: the running joke among data scientists is that 90% of any data science project is data cleaning, and 10% actually doing anything with it. At least you guys are honest about the fact that your data is messy. When I worked as a consultant, for example, I talked with many organizations about potential data science projects, and if they said their data was clean and ready to go, chances are they were lying either to themselves or to me about how messy and haphazard their data really was. The fact that you are upfront about the messiness of your data tells me that you guys as an organization are realistically assessing where you are and what you need.”
This answer not only establishes that I have handled messy data before but also normalizes the problem in the field as resolvable by an expert (like myself) and compliments them for being up front. Answering this question confidently and positively has uniquely put me at the top of the list as the front runner candidate in some interviews. Giving a good answer to is is a perfect opportunity to endear yourself with your interviewer.
Question 4: What programming languages and/or software have you used?
Even though a technical interviewer might ask this as well, I have encountered this question most frequently among non-technical interviewers. In my experience, fellow data scientist interviewers have more insider ways of deciphering whether you do in fact know data science, but for non-technical interviewers, this question is their initial way to probe that. Sometimes, they will cling to a laundry list of software and/or languages to determine whether you are qualified.
Now, I believe that having experience using the exact combination of softwares that the data science team you would be joining uses is generally not that important a criterion for job success. For a good data scientist, learning another software system or programming language once you know dozens is not that difficult of a task. But their question is completely natural and reasonable coming from their side, so you will have to answer it.
If they open-endedly ask what softwares and languages you have use, list through the ones you have used, maybe starting with the ones you use the most often. I generally start by mentioning Python, since not only is it my favorite language for data science (see this article) but also conveys that I am familiar with programming in general.
More often, though, they might ask whether you have used X software before, often asking whether you have used each software on a list they have in front of them. I would never recommend lying by claiming that you have experience with a software you have never used, but I would recommend recasting a “No” by providing an equivalent software to it that you have worked with. Here is an example:
“No, I have not used Julia, but that is because I prefer using Python for what others might use Julia for. Python is an equivalent high-functioning programming language in complexity, and the data science teams I worked on happened to prefer it over Julia.”
This not only conveys the “No” in a bit more of a positive light, but it shows that you are familiar with the software he or she just mentioned and confident about using it to match your would-be team.
Question 5: What are you looking for in a job?
Most often, this is the last major question interviewers ask me, but I have gotten it at the beginning as well. They probably save it until the end, because the question transitions very easily to the next part of the interview: either them describing the role or you providing any questions you have.
If you did a good job laying out your thesis story in the first question, then here you simply restate it from a different angle. You already laid the groundwork, and you are just bringing it home at this point. If they ask me this at the beginning of the interview before the “Tell me about yourself” question, then I use this question to retell my thesis story from this new angle.
Here is my typical answer:
“Like I said, I am energized by figuring out how to help organizations solve complex data science problems. Over the years, I have found two concrete things in an organization help me with this. First, I thrive in stimulating work environments where I am given the space and resources to think creatively through problems. Second, I also need to be able to work with people from a variety of backgrounds and disciplines from whom I can learn from and develop innovative approaches to the problem at hand. You guys seem to provide both. [I then conclude by explaining why they seem to provide both based on what you learned about the organization during the interview, or if we have not had a chance to talk about them yet, ask about these within the organization.]”
Notice that the first sentence references my self-explanation answer to the “Tell me about myself” question. If they ask this question before I have given that spiel, I spend about 30 seconds or a minute providing a condensed self-introduction and then continue with the rest of the answer.
Conclusion
These are the five most common data science interview questions I have encountered and how to prepare for them. I have found that when data scientists give advice on how to prepare for job interviews, they often focus on preparing for highly technical, factual questions (e.g., here and here). Even though having a solid data science foundation can be important, refining your overall story thesis – who you are, what you are passionate about doing, and how that relates to this job – is far more important to advance through the interview process.
I have found that humans, even supposedly “nerdy” data scientists, tend to connect with people and stories, so if you can hook them there, they generally remember you better and are more likely to hire you. When you have a compelling story, every other question will naturally fall into place as an intuitive further clarification of that overall story.
For the last few months, I have been considering a mini-blog series on the job hunt, and (at the time I am writing this) the economic downturn resulting from the coronavirus pandemic has made a discussion on finding a job even more relevant.
In this mini-blog series, I will focus on the following topic areas in the job hunt:
Learning about potential opportunities: networking, job searching, etc.
Marketing yourself for employers: writing a resume/CV and cover letter, building a portfolio, etc.
Developing your own skill sets: Navigating whether to develop your skills and which resources to use
This blog may be useful to you if you are in the following situations:
Recently left or about to leave a job (for whatever reason) and are looking for another
Have been disconcerted at your current job (again for whatever reason) and have decided to look elsewhere
Recently graduating from school or some other kind of training and seeking to enter the workforce
Finding gigs is a regular part of your work
(Final Note: Even though my blog focuses on the integration of data science and anthropology, in this mini-series, I intend my advice for just about any industry. Data science and anthropology are where I have most experience in, though, so, of course, I might implicitly have a bias towards strategies what works in those fields.)
This is my first blog on the Job Hunt mini-series. When starting to embark on finding a new job, preparing yourself is the most important first step, so the first set of posts will focus on the initial work necessary to launch yourself going forward.
Prepare yourself both physically, including financially and logistically, but more importantly mentally and emotionally for what you are about to undertake. The job hunt is often an adventure, so readying yourself is crucial.
Here are three basic ways to prepare yourself:
Give yourself time to process what you’re leaving/left.
Take stock mentally, emotionally, and materially for the long haul.
Be patient and know what you can control.
Give yourself time to process what you’re leaving/left.
Frequently when people are looking for a job, they recently ended or would like to end some prior situation: maybe something happened causing them to resign or be let go, or they are stressed for whatever reason at their current position and thus seeking something else. In such situations, make sure you explicitly take time to process and heal from whatever you may be coming out of.
How to do so might depend on both who you are and what you have encountered. Maybe you need to replenish yourself from burnout, emotionally and/or mentally process what happened, or reassess who you are. This will take time, and that it does is in no way a negative reflection of you.
Be conscientious about developing meaningful practices that will rejuvenate you and help you process what happened. Journal, take up a hobby, talk with friends or family, or do whatever helps you. After a day of exertion, our bodies physically need to sleep at night to rebuild the muscle tissue for a new day of adventures. Our emotional and mental faculties often work similarly: taking the time to slow down and process what happened will allow you to move forward in pursuit of your next occupational adventure.
Take stock for the long haul.
Be prepared mentally, emotionally, and materially for the long haul. I frequently hear people say that it takes on average six months to find a job. At the time of writing this, the economy is bad, and it could be longer. Materially and financially take stock of how many resources you have and how you can plan to get by for a while.
Prepare yourself mentally and emotionally a long trek in finding the next job. Try to resist the urge to appease yourself with the potentially false promise of a quick turnaround and ignore any swindlers trying to sell you the same.
Telling yourself that it could take months of grueling work to find a job will help you in the long run. It’ll be much easier on you to have a shorter-than-expected job hunt than to have your high hopes for a quick out crushed.
The job search is (almost always) a long and arduous process. Be ready for that.
To me personally, the job hunt feels like a tunnel: you hope/sense that there is a light at the end of it when you will find the next gig, but you do not know when it will come. It could always be tomorrow that you get that amazing job offer or several months from now.
There is generally light at the end of the tunnel, but that doesn’t mean the experience isn’t difficult. Having unrealistic expectations will only make the dark times ahead feel all the darker.
Be patient and know what you can control.
The ancient stoic philosophers emphasized not holding yourself accountable to what is beyond your control, and I have found that the job hunt can necessitate its own version of stoicism. You can do a lot to better your application, find the right job, connect with the right people, and these are important.
But, there is always so much about it that you cannot control. You cannot fully control where employers on the other side are coming from and what decisions they make: how and where they look for candidates, what they think of you and whether they value you, or even whether organizations/companies have an open position in the first place.
One pro of the online applications is that we even more equipped to apply to positions around the world, but one con is that thousands of applications go unread. Job prospects can often come and go based on wider structural societally factors – like a failing economy – or the successes or failures of that specific organization. You can do everything right in application and still fail for reasons outside of your control.
You can and should strive to make your application as strong as possible: both presenting yourself in the best possible light and searching within your means for the best job openings for you. But, as in Richard Niebuhr’s famous prayer, possessing the wisdom to know what you can and cannot change is crucial. This requires that you be patient with yourself if and when you fail so that you can continue to pick yourself back up and try again.
Pacing yourself is crucial for finding a job, and setting appropriate goals is the best way to ensure that you have a workable pace.
The job hunt is more like a marathon than a sprint, but at the beginning many encounter a strong temptation to jump in headfirst with a full sprint. Like the Hare in Aesop’s Fable, they rush into the process, expecting a quick turnaround in a few days or weeks, if only they can complete everything they have to.
Sometimes this works and they do find a job, and if so, more power to you. But, frequently this simply exhausts the person after the initial transition sets in. You cannot sprint your way through a marathon but must learn to pace yourself, lest you deplete critical energy during your initial charge.
On the other extreme, the job hunt seems uniquely set up to enable procrastination. Fixing your resume, reaching out to someone, filling out that online job application, etc. are important but frequently have undefined deadlines, meaning that you can always put it off until tomorrow. This fact coupled with the emotional energy needed to put oneself out there means that many procrastinate on key activities. This can include the exhausted sprinters who, after the initial high of their sprint crashes, often settle into an equally ineffective procrastination.
Making and keeping effective goals is the answer to both pitfalls. For sprinters, well-paced goals channel your initial energy into something productive while pacing yourself in the long run, and for procrastinators, daily and weekly goals keep you productive now at chipping away the necessary long-term complex tasks.
What I Do
So then, how do you set proper goals for the job search? Here is a breakdown of how I typically set goals:
I break down what I need to do into a series of tasks: networking/connecting with people, submitting job applications online, working on my resumes and cover letters, etc. (I will discuss the importance of and break down how to do each these in more detail in later articles.)
For quantifiably measurable tasks, like connecting with people and applying to jobs, I set a reasonable weekly goal like the following:
Reach out to 20 people a week, a.k.a. 4 people a day during the week (taking the weekends off)
Apply to 12 jobs a week, a.k.a. 3 a day during the week
Now, I prioritize reaching out to people over submitting online applications, because I have found networking to lead to more jobs interviews than submitting online applications, something I will discuss in more detail in a future article.
For qualitatively measurable tasks, like perfecting my resumes and cover letters, I generally set a certain amount of time per day/week to work on until that task is complete. For example, I might work on my resume for 25 minutes a day, for example, until the version I have for each type of job is of the quality I desire. This allows me to slowly chip away at the task.
The point of these goals is to give me the daily and weekly impetus I need to work through a much more complicated task. In the job search, there will be many necessary activities that I can always put off until tomorrow, which means that if I am not careful, I will put them off indefinitely.
By setting goals, I break down each complex task into smaller, manageable and tangible parts that I can slowly chip away at every day. Editing for 25 minutes or reaching out to 4 people is not that difficult, overwhelming, or time-consuming to complete right now, so every day I can easily manage it. The effects of these smaller parts are small at first, but over the course of a few weeks, they snowball into tackling the wider task/goal.
Goal Setting Yourself
So, how do you set reasonable goals, then? Well, the details of the goals you make are up to you and what you can handle given your schedule.
First, however, I would recommend you list out all the tasks you need to complete in order to find a job. For me, in the example above, that was networking, submitting online applications, and writing resumes and cover letters. What do you need to do?
Searching for jobs and bettering one’s application (which these three activities are examples of) are two common types of activities that job seekers must perform, so they are a good place to start. Later articles in this series will discuss several potential activities to consider in later articles as well.
After mapping out what you need to do, I would recommend the following criteria for thinking through the process:
1. Sufficiency: Give yourself enough time to work on the given task each day to slowly chip away at it overtime. (For example, reaching out to 1 person a day would not enable me to complete my overall networking task quick enough, but 4 people a day could.)
2. Completable: At the same time, make sure your daily goal is completable each day. (For example, reaching out to 20 people a day would be too high for me, given my schedule, but 4 people a day I have found perfectly doable even on hard days)
3. Resting and Reevaluating: Officially include time to rest from your goals and reevaluate what is working and not working about them. (Notice I finished all my weekly goals during the week and did not work during the weekend. This both allowed me time to rest and recuperate from the job hunt and gave me space to disengage for a few days to reassess my goal setting. One should remove oneself from one’s goal setting rhythm periodically, because such space allows you to reassess whether that rhythm is working. It is smart to graft such breaks officially into your goals, and for many people, weekends for a natural time to do that. Do it when it makes the most sense for you, though.)
Sufficiency: Give yourself enough time to work on the given task each day to slowly chip away at it overtime.
(For example, reaching out to 1 person a day would not enable me to complete my overall networking task quick enough, but 4 people a day could.)
2. Completable: At the same time, make sure your daily goal is completable each day.
(For example, reaching out to 20 people a day would be too high for me, given my schedule, but 4 people a day I have found perfectly doable even on hard days.)
3. Resting and Reevaluating: Officially include time to rest from your goals and reevaluate what is working and not working about them.
(Notice I finished all my weekly goals during the week and did not work during the weekend. This both allowed me time to rest and recuperate from the job hunt and gave me space to disengage for a few days to reassess my goal setting. One should remove oneself from one’s goal setting rhythm periodically, because such space allows you to reassess whether that rhythm is working. It is smart to graft such breaks officially into your goals, and for many people, weekends for a natural time to do that. Do it when it makes the most sense for you, though.)
Conclusion
The job hunt is often a marathon, taking several months, so setting sufficient yet completable goals with adequate rest is the most important single activity you can do to pace yourself.
Goal setting prevents you both from starting out sprinting way faster than you should and thus just tiring yourself out in the long run, and from procrastinating on going forward at all.
Finding a job is doable but prepare yourself. It can be a long journey, so you need to treat it as such.
What are you looking for in a new job? Looking for jobs provides a unique opportunity to either refine what one has been doing or to explore something new. Many people I have talked with who are searching for a job struggle with determining what they want: ranging from new graduates just entering (or reentering) the job market to people discontented with their current work. In addition, I have seen even more people, even those completely confident in what they do but for whatever reason lost their current position, struggle to articulate what they are looking for to potential employers.
This is a self-reflection activity to address both of those: think through what you want to do and articulate it concisely and passionately to others. It helps form your vocation, not only guiding what kind of positions you look for but also illuminating your overarching story you will tell others during interviews.
Step 1
List out five activities that you have done in the last few years that inspire you.
Step 2
List out five activities that you have done in the last few years that have drained/frustrated you.
Step 3
For each activity on both lists, then write out what about it inspired you and what about it drained you.
Step 4
Look through both sets of lists for common features.
Step 5
Synthesize these common features into a one- to two-sentence story.
Step 6
Tell this story to others who are close to you and practice it when appropriate with others.
Here is how it works:
Step 1: List out five activities that you have done in the last few years that inspire you.
An activity can be just about anything you have done: a job, a course you have taken, a specific project you have worked on, a hobby or pastime, volunteer activity, something you have done with family or around your home, and so on.
By “inspire,” I mean that the activity gave you energy, galvanized you, motivated you, gave you passion or inspiration, or otherwise gave your life and fulfillment doing it. This likely meant that you enjoyed the activity, but it is something deeper: that the activity gave you positive meaning/purpose, energy, motivation, or fulfillment, more than simply being fun. You felt alive while doing it.
These activities may overlap: for example, you may mention both an overall job you held that inspired you and a specific project you worked on in that job that was especially fulfilling, or both an inspirational course you took and a particular project in that course that was particularly motivating. Just about anything you have done could be an activity.
Step 2: List out five activities that you have done in the last few years that have drained/frustrated you.
Like with the five energizing activities, these may be just about anything and may even overlap with the previous list (e.g. a job might energy you overall but a part or aspect of it might have drained you). By “drained/frustrated you,” I mean the exact opposite: instead of energizing or galvanizing you, it sucked the life out of you, maybe leaving you feeling depleted, frustrated, or empty.
Note, however: Draining and frustrating you is not the same as simply tiring you. We often feel tired after doing any intense activity, whether we enjoy that activity or hate it. For example, I love playing basketball, but after playing for several hours, I will feel tired, even though I still felt stimulated while playing. Often being tired is simply the result of a full day’s work, and often many activities that fulfill or motivate us make us especially tired as well because we do them enthusiastically. An activity that drains or frustrates you takes motivation or passion from you while doing it: maybe feeling like it is sucking something out of you to complete.
Step 3: For each activity on both lists, then write out what about it inspired you and what about it drained you.
Yes, I mean write out both what inspired and drained you for the items on both lists. For the inspired activities, likely listing what about it inspired you will be easier than listing what about it drained you. But, even the most amazing activities have something about them that drained you. Thinking about both the good and the bad of each activity is crucial to self-reflect on what you want to do. Since these activities have been inspired for you, the pros will likely outweigh the cons, yet some cons must almost certainly exist.
Likewise, for the activities that drained you, list out both what inspired and drained you even if the latter outweighs the former.
I typically treat this like a brainstorm session writing out whatever comes to mind. For each activity, I make a column for inspired and another for draining and list out as many aspects I can think of for each as they come to mind.
Step 4: Look through both sets of lists for common features.
Take some time away from the list, maybe a day or so if you can. When you are ready, reread it, analyzing for common themes. Are there any common patterns to what inspired you between the activities? Are there any patterns in what drained or frustrated you?
After initially looking through it yourself, feel free to show the list to someone or a couple people who know you well: like a spouse, other family member, or close friend. Ask him or her what patterns he or she notices and what he or she thinks of your patterns.
Before giving your thoughts, I would recommend first asking your confidant what patterns he or she notices, simply listening and taking notes, without commenting much. Then only after he or she is finished share the ones you found and ask his or her take on those. That way you can hear your confidant’s initial thoughts unhindered before you influence his or her perspective by telling your own.
Write all these patterns out: both what you noticed and what anyone else you showed it to noticed. Then see whether there are any common features between you guys. Feel free to list those out as well.
Step 5: Synthesize these common features into a one- to two-sentence story.
Your next step is to organize these different features into a story: determining what the various items on both lists have in common and synthesizing these into a cohesive whole.
Here are a few potential questions to ask:
Are there any similarities between what energized you, like similar types of activities that gave you energy? For example, maybe many of the activities that inspired you involve working and communicating with others, or solving a complex problem, or developing or organizing something, or logical or analytical work, a technical skill, and so on.
Likewise, are there any similarities among the activities that drained you? Maybe one of the types of activities I gave in the first question frequently drained you?
Are there any connections between the activities that energized and drained you? This is often the case. For example, for me, coming up with innovative ways to solve complex problems energizes me, but following repetitive procedures often drain me (see my next article, where I will do this activity myself as an example, for more detail). These are flip sides of the same coin: Developing innovative and unique strategies energized me, and its opposite, following rote procedures, drain me. Likewise, see whether any of the inspiring and draining themes mirror each other.
Now synthesize these themes into a cohesive one- or two-sentence story about who you are and what you like to do. Think of this as like a thesis statement for an essay, summarizing the main points of who you are in a way that you can go into more detail on if someone’s interested.
My explanations might use the following types of sentence structures. Feel free to use these as inspirations to get a sense for how to structure your ideas succinctly, but if you have other ways of phrasing it, that is fine as well:
“I have a passion for _, but I get frustrated when _.”
“_ gives me energy.”
“_ makes me feel stifled.”
“I tend to appreciate roles that involve _, _, and_.”
“I should avoid roles that involve _, _, and _.”
To do this, I typically employ the following strategy:
First, I talk it out with a friend or family member (likely the same person or people I shared my notes with for Step 4) or, if need be, by myself. I start by simply explaining all the items on the list one by one.
Second, without looking at my notes, I describe myself completely from memory to my friend/family member. I pretend that I am introducing myself in an interview, starting with something like, “Hello, my name is Stephen. I am passionate about…” Then I analyze what I changed when I spoke off script: What made the cut; how did I phrase things in the moment, what seemed the most important to talk about, and so on?
Typically, the first time I introduce myself, it never comes out right. Maybe I begin a certain sentence but realize mid-stream that I need to start over and phrase it differently. If that happens to you, that is fine. The first time you do this often feels awkward, but working through the kinks of your spiel out now in front of a trusted confidant or by yourself is much better than stumbling through your words in a job interview for a role you really want.
I then go through that self-summary a few more times until I have a better sense for what to say. If I began one way but had to correct myself, or if I stuttered through a part, I repeat it again and again until I phrase it the way I want, and I am confident in my delivery.
Once I have my spiel, I then cut it in half. When I have done this exercise with others, I often notice people’s spiels are around two-three minutes and about twelve or so sentences. Awesome, there is a time for such long descriptions – like that infamous first question in an interview, “Tell me about yourself.” But, for now cut it in half. If you used twelve sentences, now use six or if you took three minutes, now say it in a minute and a half. Practice that a few times until you feel confident.
Then, once you get used to that shorter length, cut it in half again. Repeat this until your explanation is one- to two-sentences and/or no more than thirty-second long. That is your synthesis summary. Make sure you write it down and feel free to practice it until you can give it in your sleep. This will form the backbone of how you describe and sell yourself to employers.
Finally, think through what types of roles would match this: inspiring you while avoiding to the greatest extent possible what frustrates you. Some energizing passions lend themselves to certain positions and industries (or to avoiding others), but in my experience at least, many positions could match someone’s passions in multiple industries or fields.
Instead, it primarily tells you what you what to look for in the organizations and positions you consider. For example, if you are a data scientist, this tells you what type of data science job might work for you (or if you are considering leaving the field, what types of positions in any new field you might consider entering).
Your synthesis provides the criteria of what to look for in whatever industry or field you are considering. Thus, this will be what you communicate to others when talking with them about potential jobs.
Step 6: Tell this story to others who are close to you and practice it when appropriate with others.
This summary forms the backbone of the story you tell others. When you network or go into a job interview, you will use this to describe yourself to others, so make sure you practice it until you can give reflexively. You can practice it by yourself, but I at least generally prefer practicing in front of others: friends, family, or the first people you network with so that I can see how other people respond and adjust what I say accordingly.
Feel free to develop a few different degrees of explanation in case you need them: a 10-second description, 30-second description, 1- to 2-minute description, and even a 3- to 5-minute much more detailed monologue. You will use different ones depending on the social situation and their degree of interest. In a future article, I will discuss how to describe yourself compellingly in more detail.
For now, pat yourself on the back: You did a lot. This self-reflection is crucial for determining what kind of role you want going forward and providing the narrative skeleton of how you present yourself to others. Such intense self-reflection can be taxing work, but it is ultimately worthwhile.
In this article, I complete the occupational self-reflection I described in Part 4 as an example of what such a reflection might look like. Your story won’t be mine, so feel free to craft the reflection to fit your needs. This is just one sample of what it could look like.
Activity Overview:
Step 1
List out five activities that you have done in the last few years that inspire you.
Step 2
List out five activities that you have done in the last few years that have drained/frustrated you.
Step 3
For each activity on both lists, then write out what about it inspired you and what about it drained you.
Step 4
Look through both sets of lists for common features.
Step 5
Synthesize these common features into a one- to two-sentence story.
Step 6
Tell this story to others who are close to you and practice it when appropriate with others.
Step 1: List out five activities that inspire you.
Show Rate Predictor at BronxCare
Master’s Practicum
Ethno-Data Blog
Writing a Sitcom
Networking
Descriptions:
(Note: You wouldn’t likely need to explain what each activity is to yourself, since, well, you are the person who did them. Just listing them is fine. But I am describing my activities to help give you, reader, context since otherwise, you won’t likely know what I did.)
Show Rate Predictor at BronxCare: This was a major project I worked on at BronxCare. I worked with a clinic to build a machine learning show rate predictor that both calculated the probability an upcoming appointment would occur and estimated the number of appointments to expect for every doctors’ shifts. I started by conducting ethnographic user research into the problem to figure out exactly what they need, built two machine learning algorithms to provide the most useful information, and worked with a team to develop the app to communicate that findings to schedulers in real-time as they schedule.
Master’s Practicum: The University of Memphis’s Anthropology Department required a practicum project, a 300-hour or more project with an organization for the master’s degree and documenting a detailed research report on the work. I did my practicum with Indicia Consulting in the summer of 2018. Here is my full report for your reference.
Ethno-Data Blog: I am referring to this blog you are reading. So, if you are reading this, you have found it and know what it is.
Writing a Sitcom: I have been writing an animated sitcom, which a few of my artistic friends and I are planning on developing.
Networking: By this, I am thinking of the general activity of networking with other people to learn about their work and find connections with them. I do this particularly vigorously whenever I am looking for a job.
Now, yes, these are a range of projects: including a work project, a personal project, a school project, and finally a nebulous, informal activity like networking. This is by design, since encompassing a wide variety of different types of activities allows me to think about different facets of my life. Feel free to choose among any type of activities that would be most helpful for you.
Step 2: List out five activities that you have done in the last few years that have drained/frustrated you.
Comprehensive Exams
UX Research Consulting Project for Thriving Cities Group
Data Pulling at BronxCare
Retention Research Project with ServiceMaster
Secondary Math Teaching
More detailed explanations for your reference:
Comprehensive Exams: The University of Anthropology required a written comprehensive exam to graduate with a master’s degree. One must write a four in-class essays over the series of two days and then defend your answers to your committee.
UX Research Consulting Project for Thriving Cities Group: Thriving Cities Group was seeking to build an app to help non-profits coordinate with potential funders and chose Memphis, Tennessee as the starting place to launch their beta-version. They hired me as a UX researcher in preparation for this launch. I really enjoyed the UX research I did, but I also learned a lot about what I need when conducting research as a consultant with organizations.
Data Pulling at BronxCare: As a data scientist who specialized in developing machine learning and statistical models for data, my role as a data scientist at BronxCare typically did not require simple data queries to pull data for a project. But, occasionally it would. These would generally be one-time SQL queries into the database system (since another team would generally conduct repeating queries). This was my least favorite aspect of my job there.
Retention Research Project with ServiceMaster: As a data scientist, I worked on a year-long project building a model to predict and understand retention. I appreciated the idea of the project and the nature of the work, but its implementations had some issues: the abrupt departure of my manager who had commissioned the project, the lack of managerial buy-in for the project above him, issues collecting the necessary data, and departmental politics. These led to the project being overall frustrating, although also a learning experience on aspects of organizational research projects frustrate me.
Secondary Math Teaching: I taught secondary math in both Gary, IN and Chicago, IL for a few years before deciding to move into data science. Through this, I learned middle school and high school teaching is not a good fit for me.
Notice again that this list covers a variety of different types of projects from different contexts and times in my life. Feel free to do the same.
Step 3: For each activity on both lists, then write out what about it inspired you and what about it drained you.
Here are what aspects of each list energized me and frustrated me.
Note: Yes, I wrote both what energized me and what frustrated me for both lists. Even the most life-giving activities have aspects about them that frustrates you and the most frustrating activities have positive aspects. You learn a lot about thinking about the ugly in the good and the good in the ugly.
Energizing Activities
Energizing Activities
What Energized Me
What Frustrated
Show Rate Predictor at BronxCare
Trying to break down and solve a complex problem
Figuring out the optimal machine learning models to useDesigning the software’s architecture and design
Presenting/selling it to those in the clinic and receiving their buy-in/support
Bureaucratic red tape to get the proper software access to build the app
Internal politics within the organization in getting the project off the ground
Master’s Practicum
The work itself: solving the problem and completing the task with Indicia
Researching and seeking to rethink the historic relationship between anthropology and data science (my key intervention in my report)
Presenting my work at conferences and the recognition through the various awards I received for innovative research
Committee members seemingly unwilling to entertain the thought of rethinking the discipline and its relationship to data science
Committee members trying to cast my work as not anthropology because it did not fit their pre-assigned mold of what anthropology is
Ethno-Data Blog
Researching and developing interesting ideas, analysis, content, and explanations
Writing blog postsEspecially writing this Job Hunt mini-series
People coming to me saying they appreciated my blog and/or to ask questions about it
Using the WordPress software interface, particularly difficult is transferring articles from Word Documents in which I originally write to WordPress
Rushing to meet posting deadlines
Writing a Sitcom
Developing the characters and their back stories
Developing the overarching story structure for the show, outlining plots of specific episodes, and developing specific scenes
Meeting with other artists to build a teamFeeling accomplished after finishing a draftSeeing other people excited about the idea
Editing: constantly polishing over the dialogue to make it shine (necessary but can be agonizing)
Trelby’s interface (the screenwriting software I use)
Networking
Reaching out to and connecting with people
Learning about other people and their work/experiences
Thinking through ways to work together to solve problems
Its cyclic nature: constantly repeating the same cycle of reaching out to new people after concluding with current connection
Monotony of reaching out: generally having the repeat the same set of tasks with minor yet hard to automate tweaks
Frustrating Activities
Frustrating Activities
What Energized Me
What Frustrated
Comprehensive Exams
Researching and developing the ideas to write about
Writing out my prepared essays
Wanting to talk about my intellectual journey and what anthropologists should learn from other disciplines and ways of thinking in order to think through how to engage with others in the world
Some of my committee members tried to force how I would think about things in the prompt by including what seemed like false premises into the questions themselves
Some of committee members seemed to want me to parrot why anthropology is great and superior to all other disciplines
Already dealing with burnout at the start of the assignment
UX Research Consulting Project for Thriving Cities Group
Hearing about people’s work at various non-profits around Memphis and their stories
Analyzing issues with the app
Working with engineers to come up with innovative solutions to the problems users brought up
The team seemed to be trying to find a problem that matched their “solution” they have already built instead of seeking to understand what issues/needs people have and then crafting a solution to those needs
Top-down and forward-moving marching orders, which, in this instance, ignored the app’s severe issues with users on the ground
Data Pulling at BronxCare
Figuring out how to make Python scripts to automate these processes
Building and analyzing models on the data after getting it together
Tedious
Unnecessarily time-consuming yet trivial and unstimulating intellectually
Data was stored in inconsistent and frustrating ways to access later
Retention Research Project with ServiceMaster
Planning and scoping out how to do the project
The ethnographic portion of the project where interviewed and observed customer service representatives to learn about their experiences/expertise in communicating with customers
Networking and collaborating with people from other teams to obtain data needed for the project and to learn about their own related projects
Felt like the work connected well with the goals and bottom-line of both organization as a whole and my team specifically
When developing the research questions for the project, I felt like my supervisor could only think of limited, close-ended questions, instead of thinking open-endedly about the project. This seemed necessary given the complexity of the issue at hand.
The research questions I was given for the project overly reductionist and simplisticThe data was half-hazard and messy, which made analysis with it difficult.
Frustrating inter-departmental politics to access and use of the data I got
Secondary Math Teaching
Public speaking
One-on-one teaching and mentoring
Helping students develop the critical thinking skills to solve complex problems themselves
Managing childish/immature student behavior
The tedium of grading
Teachers did not have enough time to collaborate in the school.
Step 4: Look through both sets of lists for common features.
Looking through them, I found the following common features within both columns:
What Energized Me
What Frustrated
Talking with people and learning about what they are doing, whether through interviews, ethnographic research, or another means.
Networking and collaborating with others on a project
Analyzing and developing strategies to solve complex problems (e.g. whether the problem is data science-problem, mathematical/statistical, social or “people” problem, and so on.)
Communicating solutions to others (both conversationally and through public speaking) Writing creatively
Monotony, tedium, and rote work
Having to follow a seemingly unproductive and non-innovative procedure
Not having an innovative strategy I have been developing be understood or appreciated by those I am working with, especially my supervisor Feeling my innovative ideas were shut down or attempted be shut down due to close-mindedness and the inability to think outside of the “conventional” ways of doing things
Step 5: Synthesize these common features into a one- to two-sentence story.
I did this synthesis twice, first writing a very large and cumbersome one-sentence version and then shortening it to make it more manageable.
Synthesis #1 – Large Cumbersome One-Sentence Version:
I am passionate about developing innovative ways to solve complex problems with others in situations for which the conventional approaches do not work, but I become frustrated when those with whom I am working (especially supervisors) do not understand or appreciate that innovation and try to enforce conventional ways of thinking that they have always employed.
Synthesis #2 – Final, Shortened Version:
My passion in a job is to develop innovative ways to solve complex problems, but I become frustrated when those with do not understand/appreciate that innovation and try to enforce conventional approaches.
Step 6: Tell this story to others who are close to you and practice it when appropriate with others.
I cannot easily show this stage in a written article like this, but suffice to say that it is extremely important to show and practice this in front of trusted family and friends. You learn a lot from their feedback.
“Data science is doable,” a fellow attendee of the EPIC’s 2018 conference in Honolulu would exclaim like a mantra. The conference was for business ethnographers and UX researchers interested in understanding and integrating data science and machine learning into their research. She was specifically trying to address a tendency she has noticed– which I have seen as well: qualitative researchers and other so-called “non-math people” frequently believe that data science is far too technical for them. This seems ultimately rooted in cultural myths about math and math-related fields like computer science, engineering, and now data science, and in a similar vein as her statement, my goal in this essay is to discuss these attitudes and show that data science, like math, is relatable and doable if you treat it as such.
The “Math
Person”
In the United States, many possess an implied image of a “math person:” a person supposedly naturally gifted at mathematics. And many who do not see themselves as fitting that image simply decry that math simply isn’t for them. The idea that some people are inherently able and unable to do math is false, however, and prevents people from trying to become good at the discipline, even if they might enjoy and/or excel at it.
Most skills in life, including mathematical skills, are like muscles: you do not innately possess or lack that skill, but rather your skill develops as you practice and refine that activity. Anybody can develop a skill if they practice it enough.
Scholars in anthropology, sociology, psychology, and education have documented how math is implicitly and explicitly portrayed as something some people can do and some cannot do, especially in math classes in grade school. Starting in early childhood, we implicitly and sometimes explicitly learn the idea that some people are naturally gifted at math but for others, math is simply not their thing. Some internalize that they are gifted at math and thus take the time to practice enough to develop and refine their mathematical skills; while others internalize that they cannot do math and thus their mathematical abilities become stagnant. But this is simply not true.
Anyone can learn and do math if he or she practices math and cultivates mathematical thinking. If you do not cultivate your math muscle, then well it will become underdeveloped and, then, yes, math becomes harder to do. Thus, as a cruel irony someone internalizing that he or she cannot do math can turn into a self-fulfilling prophecy: he or she gives up on developing mathematical skills, which leads to its further underdevelopment.
Similarly, we cultivate another false myth that people skilled in mathematics (or math-related fields like computer science, engineering, and data science) in general do not possess strong social and interpersonal communication skills. The root for this stereotype lies in how we think of mathematical and logical thinking than actual characteristics of mathematicians, computer scientists, or engineers. Social scientists who have studied the social skills of mathematicians, computer scientists, and engineers have found no discernable difference in social and interpersonal communication skills with the rest of the world.
Quantitative and Qualitative Specialties
Anyone can learn and do math if he or she practices math and cultivates mathematical thinking.
The belief that some people are just inherently good at math and that such people do not possess strong social and interpersonal communication skills contributes to the division between quantitative and qualitative social research, in both academic and professional contexts. These attitudes help cultivate the false idea that quantitative research and qualitative research are distinct skill sets for different types of people: that supposedly quantitative research can only be done “math people” and qualitative research by “people people.” They suddenly become separate specialties, even though social research by its very nature involves both. Such a split unnecessarily stifles authentic and holistic understanding of people and society.
In professional and business research contexts, both qualitative and quantitative researchers should work with each other and eventually through that process, slowly learn each other’s skills. If done well, this would incentivize researchers to cultivate both mathematical/quantitative, and interpersonal/qualitative research skills.
It would reward professional researchers who develop both skillsets and leverage them in their research, instead of encouraging researchers to specialize in one or the other. It could also encourage universities to require in-depth training of both to train their students to become future workers, instead of requiring that students choose among disciplines that promote one track over the other.
Working together is
only the first step, however, whose success hinges on whether it ultimately
leads to the integration of these supposedly separate skillsets. Frequently,
when qualitative and quantitative research teams work together, they work mostly
independently – qualitative researchers on the qualitative aspect of the
project and quantitative researchers on the quantitative aspects of the project
– thus reinforcing the supposed distinction between them. Instead, such
collaboration should involve qualitative researchers developing quantitative
research skills by practicing such methods and quantitative researchers similarly
developing qualitative skills.
Conclusion
Anyone can develop mathematics and data science skills if they practice at it. The same goes with the interpersonal skills necessary for ethnographic and other qualitative research. Depicting them as separate specialties – even if they come together to do each of their specialized parts in a single research projects – functions stifles their integration as a singular set of tools for an individual and reinforces the false myths we have been teaching ourselves that data science is for math, programming, or engineering people and that ethnography is for “people people.” This separation stifles holistic and authentic social research, which inevitably involves qualitative and quantitative approaches.
