I was working as a Product Manager for my current company. In that role I provided relatively advanced data analysis for several departments, often used BI software, did some coding, and worked on some data modeling tasks. I regularly presented to managers of all levels as well.

Becoming a 'Data Scientist':

One day, a director in IT approached me about a Data Scientist position. At the time, I had worked for the company for three years. I agreed to the position because it involved a 30% pay bump and I was interested in focusing more on predictive and prescriptive analytics in my career. The job description was somewhat typical for a Data Scientist position although there were some minor issues that concerned me.

The reality of the 'Data Scientist' position:

It turns out that this IT director lied about the responsibilities of this position. He really wanted me to solely work on development, analysis, and project coordinator work centered around Business Intelligence.

He had no desire to hear anything about Data Science.

Whenever I took a class related to Data Science, he told me it had nothing to do with my job and always hesitated to provide financial assistance. He didn't want me to use tools and methods commonly associated with Data Science such as Python, machine learning, statistics, data wrangling, etc.

This went on for a year and a half.

My situation now:

Last week, this IT director was fired, most likely because he was caught in some of his lies. There were issues he caused for people throughout the company, the ROI of some software investments were very low, and he was in constant conflict with other teams-—including an analytics team I used to work for under the Sales department of that company.

He probably wanted me off that team as a way to attack the manager of that analytics team.

This IT director was also EXTREMELY upset about people using data sources that didn't come directly from his department. Almost a year before that, his direct manager (a CIO) was told to resign from the company...

Now I work for a manager (a person the aforementioned IT director also hired) who is continuing where the fired director left off when it comes to giving me responsibilities that have nothing to do with Data Science. It seems that my current manager is very competent when it comes to the core purpose of his team, but my current responsibilities are a definite step down from those of my previous role in other departments of that company and they have nothing to do with data science.

Also, the work environment of the IT department I'm in is also relatively toxic and lacking in trust-—partly as a result of the past leadership.

These are my questions:

  1. How should I go about my job search when I have a "Data Scientist" job title, but my superiors did not give me adequate work to build Data Science experience that recruiters and hiring managers would expect? I've taken four classes related to Data Science, but the studying I do on my free time doesn't seem like enough compared to other people applying for Data Science roles who can work on Data Science projects for 40+ hours a week every week.

  2. Should I focus on jobs geared more towards Analytics for now to ensure that I can have an easier time being hired quicker? These jobs could include Analytics Consultant, Program and Sales Analytics Specialist, Senior Analyst, etc.

  3. On my resume, should I reserve a relatively small amount of space to the Data Science classes I took so that I don't look over-qualified?

  • @OP I proposed an edit to make it a bit easier to read and see the course of events (to hopefully get useful answers) but needs to be approved either by yourself, or someone with a bit more 'rep' than I have! :) Commented Jan 17, 2020 at 21:46
  • Thank you, seventyeightist. I accepted your edits. I somehow forgot the importance of including headers in relatively long posts. Commented Jan 18, 2020 at 6:08
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    In the real world 99% of “data science” is doing exactly what you do now, BI and analytics. That world of blowing everyone’s minds with the machine learning you did in your Jupyter notebook exists only in blogs. You will discover this for yourself sooner or later.
    – Gaius
    Commented Jan 18, 2020 at 20:33
  • @OP, having thought about this question a bit more during today: Why do you conclude that the director 'lied' about the responsibilities (rather than, say, not understanding how a Data Scientist differs from a BI, analysis, data wrangling position and just googled up a generic 'data scientist job description' -- I presume the DS role didn't already exist in the company so a new JD had to be created)? You also say that he was fired "most likely because [of] his lies" - why do you think this? Had he been lying other than this? (not just "constant conflict"..) Commented Jan 18, 2020 at 20:55
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    @TechnicalTim The reality of it is that the majority of data science work (by volume of work hours) is drudgery that doesn't take many special skills to carry out (even if they might take special skills to plan correctly). In my experience it's maybe ~20-30% of a project at most that is legitimately in the statistics/machine learning domain. It's also hard to plan around-- it's research, which means that null (or otherwise unusable to a business) results are valid ends. What many employers really want is data dredging capability plus optimism, whatever they think or claim to want.
    – Upper_Case
    Commented Jan 20, 2020 at 17:16

3 Answers 3


The best answer to questions like this doesn't really change much even though people asking questions present sometimes very different back stories. We can't directly answer the question for you, but we can provide you with a roadmap to get to your own best answer.

Before anyone thinks about your actual three questions though, you need to take a step back and ask yourself a few questions:

  • What are my ultimate career goals? What is my dream job?
  • How "far" from my current job is that dream job?
  • What next step makes the most sense?

You can answer these questions in isolation from looking at actual job postings. To put it another way: Answer these before you start looking for a job. Be detailed in your answers. By the time you've answered these three questions, you should have an ideal next-step job in mind, and you should be able to write out a generic job description for that next step job. While you're doing that, it can be helpful to look at job postings to get idea, but resist the urge to start applying until you're done preparing - a poorly structured resume could cost you an opportunity, whereas a few hours or days of work could position you to nail your next job.

Once you have that generic job description framed up, rewrite your resume in the context of that job. Focus on elements that the job description mentions, and focus on expressing your strengths in a way that's relevant. Resumes are sales tools - you are selling yourself to a potential employer. You need to be honest, but you also need to be relevant for what this person is looking for.

This is the point in the process where you can answer your three questions, for yourself! But here are some suggestions:

How should I go about my job search when I have a "Data Scientist" job title, but my superiors did not give me adequate work to build Data Science experience that recruiters and hiring managers would expect?

Don't get hung up on titles or whether or not your experience matches the title you have. Instead, focus on the work you actually did, and the results you got. Write about work and results that is relevant to the opportunity you want.

Should I focus on jobs geared more towards Analytics for now to ensure that I can have an easier time being hired quicker?

If it wasn't obvious yet, you have to answer that. Don't just focus on what's easy to get. Focus on something that's reasonable, but which you're interested in. Then tailor your resume, application, and interview answers to that opportunity.

On my resume, should I reserve a relatively small amount of space to the Data Science classes I took so that I don't look over-qualified?

If you're trying to get a data science job, then absolutely include it. Don't worry about looking over qualified, just focus on showing the things from your experience that match the position you're going after.

People hiring into entry level data science roles will not expect you to have years of directly relevant experience, but they will scan a resume that's at least in the right ballpark and look for things that are close enough to justify an entry level position. If that's the kind of job you want, your mis-alignment between your current title and your tasks won't be an issue.

Finally, before you proceed, spend some effort thinking not just about the ideal next position, but also the ideal next employer. It sounds like you were frustrated by internal politics between departments. While that can be hard to detect as a candidate, you can certainly consider if there are any other factors you especially want (or do not want) in your next employer. Don't be afraid to be picky, and to do what you can to ensure you'll actually be happy at your next job.

For instance - since you also expressed that you were unhappy with the types of work you are doing now, despite the Data Science title, make sure you ask questions about:

  • the types and sizes of projects you'll be working on
  • the makeup of teams you'll be a part of
  • the tools you'll have in your day to day job

and so on. These questions can help you understand if your new employer is actually hiring for the type of work you want, versus hiring for a title that doesn't match with what you think the job consists of.

  • 1
    That's an amazing answer, dwizum. Not only did you guide me through the cocerns I had, but you also provided excellent input regarding the context and future implications related to the concern. I can't thank you enough for that. Commented Jan 17, 2020 at 21:11
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    Great answer (and somehow I knew you were the author before scrolling down!) I'm curious what you think the canonical question wording is for "questions like this [which don't] really change much even though people asking questions present sometimes very different back stories"? Commented Jan 17, 2020 at 21:29
  • @seventyeightist - thanks for the kind words. I'm not sure if there's a single canonical wording for a question like this, because the askers often approach it from very different directions. The similarity that stands out to me is an asker implying that they're not sure what their next step should be career-wise, usually with a focus on titles, and without indication of having a thought process to decide for themselves what they will find more rewarding. I think this is the classic case of someone wanting an answer, but really needing a process with which they can get their own answer.
    – dwizum
    Commented Jan 21, 2020 at 13:52

I think 4 classes is plenty. I know people who only took one and got hired as Data Scientists right out of school.

Good on you for not putting up with that shitty place anymore, but I think you are underestimating yourself and the overestimating the degree of difficulty that Data Scientists have to deal with on an average job. There is a learning curve but typically it's quite fast. Typically the style and sometimes even language of the code you write is highly specific to your workplace so I wouldnt worry about the lack of experience too much.


My personal recommendation is to write in your CV the exact position you have AND describe in responsibilities and tasks what you do. And be prepare to explain on interview what you do/did without mentioning much "Data Scientist". Of course you should add your courses and certificates and during interview explain the fact you do not have experience in the area.

When you search job search it based on your experience and knowledge.

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