I worked as a data scientist at a hospital in New York City during the worst of the covid-19 pandemic. Over the spring and summer, we became overwhelmed as the city turned into (and left) the global hotspot for covid-19. I have been processing everything that happened since.
The pandemic overwhelmed the entire hospital, particularly my physician colleagues. When I met with them, I could often notice the combined effects of physical and emotional exhaustion in their eyes and voices. Many had just arrived from the ICU, where they had spent several hours fighting to keep their patients alive only to witness many of them die in front of them, and I could sense the emotional toll that was taking.
My experiences of the pandemic as a data scientist differed considerably yet were also exhausting and disturbing in their own way. I spent several months day-in and day-out researching how many of our patients were dying from the pandemic and why: trying to determine what factors contributed to their deaths and what we could do as a hospital to best keep people alive. The patient who died the night before in front of the doctor I am currently meeting with became, for me, one a single row in an already way-too-large data table of covid-19 fatalities.
I felt like a helicopter pilot overlooking an out-of-control wildfire.[1] In such wildfires, teams of firefighters (aka doctors) position themselves at various strategic locations on the ground to push back the fire there as best they can. They experience the flames and carnage up close and personal. My placement in the helicopter, on the other hand, removes me from ground zero, instead forcing me to see and analyze the fire in its entirety and its sweeping and massive destruction across the whole forest. My vantage point provides a strategic vantage point to determine the best ways to fight it, shielding me from the immediate destruction. Nevertheless, witnessing the vastness of the carnage from the air had its own challenges, stress, and emotional toll.
Being an anthropologist by training, I am accustomed to being “on the ground.” Anthropology is predicated on the idea that to understand a culture or phenomena, one must understand the everyday experiences of those on the ground amidst it, and my anthropological training has instilled an instinct to go straight to and talk to those in the thick of it.
Yet, this experience has taught me that that perception is overly simplistic: the so-called “ground” has many layers to it, especially for a complex phenomenon like a pandemic. Being in the helicopter is another way to be in the thick of it just as much as standing before the flames.
Many in the United States have made considerable and commendable efforts to support frontline health workers. Yet, as the pandemic progresses, and its societal effects grow in complexity in the coming months I think we need to broaden our understanding of where the “frontlines” are and who a “frontline worker” is worthy of our support.
In actual battlefields where the “frontline” metaphor comes from, militaries also set up layered teams to support the logistical needs of ground soldiers who also must frequently put themselves in harm’s way in the process. The frontline of this pandemic seems no different.
I think we need to expand our conceptions of what it means to be on the frontlines accordingly. Like anthropology, modern journalism, a key source of pandemic information for many of us, can fall into the issue of overfocusing on the “worst of the worst,” potentially ignoring the broader picture and the diversity of “frontline” experiences. For example, interviewing the busiest medical caregivers in the worst affected hospitals in the most affected places in the world likely does promote viewership, but only telling those stories ignores the experiences and sacrifices of thousands of others necessary to keep them going.
To be clear, in this blog, I do not personally care about acknowledgement of my own work nor do I think we should ignore the contributions of these medical professional “ground troops” in any way. Rather, in the spirt of “yes and,” we should extend our understanding of the “frontline workers” to acknowledge and celebrate the contributions of many other essential professionals during this crisis, such as transportation services, food distribution, postal workers, etc. I related my own experiences as a data scientist because they helped me learn this, not for any desire for recognition.
This might help us appreciate the complexity of this crisis and its social effects, and the various types of sacrifices people have been making to address it. As it is becoming increasingly clear that this pandemic is not likely to go anywhere anytime soon, appreciating the full extent of both could help us come together to buckle down and fight it.
[1] This video helped me understand the logistics of fighting wildfires, a fascinating topic in itself: https://www.youtube.com/watch?v=EodxubsO8EI. Feel free to check it out to understand my analogy in more depth.
A friend and fellow professor, Dr. Eve Pinkser, asked me to give a guest lecture on quantitative text analysis techniques within data science for her Public Health Policy Research Methods class with the University of Illinois at Chicago on April 13th, 2020. Multiple people have asked me similar questions about how to use data science to analyze texts quantitatively, so I figured I would post my presentation for anyone interested in learning more.
It provides a basic introduction of the different approaches so that you can determine which to explore in more detail. I have found that many people who are new to data science feel paralyzed when trying to navigate through the vast array of data science techniques out there and unsure where to start.
Many of her students needed to conduct quantitative textual analysis as part of their doctoral work but struggled in determining what type of quantitative research to employ. She asked me to come in and explain the various data science and machine learning-based textual analysis techniques, since this was out of her area of expertise. The goal of the presentation was to help the PhD students in the class think through the types of data science quantitative text analysis techniques that would be helpful for their doctoral research projects.
Hopefully, it would likewise allow you to determine the type or types of text analysis you might need so that you can then look those up in more detail. Textual analysis, as well as the wider field of natural language processing within which it is a part of, is a quickly up-and-coming subfield within data science doing important and groundbreaking work.
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.
