Tag: Job Seeking

Looking for a Job? The Three Online Profiles to Build that Can Help Open Doors

(My entire series on how to find a job is here. )

I have spoken with many people who are unemployed looking for a job or want to make a jump from whatever they are doing now to something better. I have already written about how to network with others to find a job, how to strategically prioritize the job application process, and how to nail an interview.

This piece will focus on how to create an online profile that can help you find a job. This two- or three-pronged social media strategy is incredibly helpful in presenting you as an expert professional in whatever field you are working in.

Step 1: Create a LinkedIn profile (and any industry specific online profiles).

Everyone needs a LinkedIn profile, especially when finding a job, so if you don’t have a profile create one. It’s a major hub for job networking in pretty much every industry.

But how to create a LinkedIn profile? For someone who is making a transition to a new field, the best strategy is to position oneself as if one has already done similar work. If you want to continue working in the type of job you’ve always been working in, that’s great; you already demonstrated your experience, but for those who want to make a transition from one field to the other, it can be best to highlight anything you have done that can seem like you have been doing that work or at least something similar.

If you have done something similar in a previous job, even if it’s just one project, blow it up on your LinkedIn description of that job. If you have a job where you can work on a side project that is related to what you want to do, then give it a try. For example, I have advised many who want to become data scientists to incorporate a data science component into whatever they are doing in their current jobs or to create a project at work they do on the side that involves data science. Thus, they can discuss their current work as involving data science work and use that to present themselves as a fledgling data scientist.

People often will describe themselves on LinkedIn as “aspiring data scientist,” “aspiring designer,” etc. Saying you are “aspiring” is the next best alternative if you absolutely cannot describe yourself as already doing it. If you are able to describe yourself as doing it, even if a minor capacity alongside other work, then do it. (And if you don’t have a job at the moment to incorporate a project into, see Step 3.) You will be treated as if your foot is in the door, not as someone knocking and waiting to be let in.

Step 2: Create any industry specific online profiles:

Also, important: if there are specific social medias for the industry you want to work in, create those as well. I’m a data scientist, and for tech, that would be GitHub. In my experience, artists often have an Instagram to showcase your art (frequently a different profile than their personal profile). Video editors often use Fiver. Some industries have specific social media where it all happens. How do you know what profile to create for the industry you want to work in?

Well, if you don’t already know, talk to people in the industry and just ask them (here’s how to network and talk to people in the industry again). They’ll tell you pretty quickly which ones.

Step 3: Work on projects in your new field

This is especially useful for those who are unemployed. Do something in the field. If push comes to shove, work on a personal project in the field, but if you can, do some part-time work helping someone else (networking helps you find these, so see that article; I keep mentioning it because it is important). You can thus list this as work experience. Never mind if it’s only a few hours a week: it’s still work experience. This helps present you as someone already in the field, circumventing the vicious cycle of entry level jobs in a field already requiring having work experience doing it.

For example, ten years ago when I took a data science boot camp during a transition period in between jobs considering becoming a data scientist, I networked extensively to learn about different professions and what people did in the industry. As such, I spoke with someone in charge of a boutique consulting firm who needed a data scientist for a project. I agreed and worked for an hourly wage maybe 4 hours a week on this project. It was actually helpful in itself because it was the first time I did data science in the “real world” outside the simulated environment of my lessons. But I also put it on my LinkedIn as my current experience (the profile didn’t make a distinction between full-time and part-time work experience anyways). My job title on LinkedIn was now officially “data scientist,” and after finishing the bootcamp in several months, I officially had six months experience working as a data scientist. This made me a beginner data scientist in interviews, not an aspiring data scientist, allowing me to circumvent the process of not having had work experience.

The project you work on can be informal. Friends or family may have a project they want you to work on that could fit this. If they officially give you money for your trouble, even if it’s only a few dollars or an ice cream cone, that is even better: on LinkedIn and your resume, it’s now a job. Even a company you started could in theory work. I don’t like the so-called “unpaid internships” because I find them exploitative, but even these could technically work. You don’t have to spend too much time working on them either. Just a few hours a week, and you’re still officially working in the field. People can often fit only a few hours a week into even the busiest schedule.

Step 4: Create a professional website/blog

This can be the most time-consuming but super important step: create a professional website or blog in the subject matter you would like to work in. If it’s data science, make a data science blog that also showcases your data science projects; if your a designer, make a blog about design that also showcases your design work; etc. This will position you as a knowledgeable person contributing ideas in the field.

For example, when I was fresh out of graduate school, looking for a job in data science, I initially created this website (ethno-data.com). My purposes for this blog have evolved overtime, but one of my initial reasons for creating it was to find a job. From that perspective, this website reaped dividends: you’d be surprised at how many people will reach out to me because they have found some of my articles interesting and have a role that might be a good fit for what I do.

Try to pick a specific niche within your field that is interesting to you personally and compelling to at least some others. It doesn’t have to be the most popular idea in the field; more important that it is something you enjoy and feel comfortable nerding out on. You can combine multiple areas of expertise you have, focusing on how they overlap (for me, that was ethnography and data science/AI). Make it reflect you. That both helps sell you and your unique contribution to the field, but it also tends to make it more interesting to you. You won’t want to post on content again and again if it’s not you.

There are so many different platforms to use to build the website (from WordPress to Bluehost to Medium to Squarespace and so on), so do your research on which one works best for you. These platforms tend to walk through the process for non-techies anyways. Set yourself a posting schedule and have at it. Whether you write articles once a week, every other week, every month, matters less. What tends to matter is that you post according to a consistent schedule.

Last but arguably most importantly, mention your articles on LinkedIn and any other social media platform you are using to network. If you post an article in your blog that week, write a statement on LinkedIn (or any other social media you are using) summarizing what you talked about and link to it. This will not only encourage people to read your pieces, but most importantly of all, it will present you online as someone contributing to the field. You are helping to advance your field. As such, people will be more likely to hire you.

Conclusion

That’s it. If you do all four of these, you will have a kick-butt online profile that will help you find a job, even if it’s a profession you are transitioning into. They are all practical things you could set up anytime. With the exception of Step 3, which sometimes requires coordination with others, you can do each of these on your own. You can even get started right now if you want.

The Four Most Common Data Science Interview Questions and How to Prepare for Them

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:

  1. 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.
  2. 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.  
  3. 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.
  4. 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.  

Photo Credit #1: Work With Island at https://unsplash.com/photos/FX2QA0TMEYg

Photo Credit #2: Free-Photos at https://pixabay.com/photos/glasses-reading-glasses-spectacles-1246611/

Photo Credit #3: geralt at https://pixabay.com/illustrations/questions-font-who-what-how-why-2245264/

Photo Credit #4: Darwin Vegher at https://unsplash.com/photos/W_ZYCEUapF0

Photo Credit #5: geralt at https://pixabay.com/illustrations/software-program-cd-dvd-disc-pack-417880/

Photo Credit #6: jenoliver777 at https://pixabay.com/photos/horses-dogs-groundwork-blaze-2888749/