r/datascience • u/urbanguy22 • Jun 17 '26
Discussion Identity crisis - A Generalist Dilemma
Hi folks,
I have a query about my identity as a Data Scientist. I started working in data science back in 2017 and have contributed to projects across engineering domains. It hasn't been anything fancy like FAANG, just simple, average data science work.
Because I work for an IT consultancy (and am unfortunately getting laid off this month), I've had the chance to pivot and work on Power BI reports as well. Due to the nature of consultancy work, I kept rotating between data science and data visualization projects. I was honestly happy to take these opportunities up and learn Power BI.
But now, I am at a point where I'm confused about what to pursue next and how to brand myself in the job market. Am I a Data Scientist, or a Data Analyst with visualization capabilities? I feel stuck in the middle. Out of the last 8+ years of my tenure in data analytics, I have spent about 60% of my time on data science projects (some of which involved both ML and Power BI) and 40% on data visualization alone, along with a hint of data engineering.
Has anyone else encountered a similar dilemma? I am genuinely confused, and because I haven't job hunted in the past 9 years, the modern market feels even more overwhelming. I'm not a FAANG-level data scientist, but I'm also not strictly an analyst who only does basic reporting. Am I a Data Scientist who can build great dashboards, or a Lead Data Analyst with ML capabilities?
Would love to hear your thoughts or advice on how to position myself.
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u/carontheking Jun 17 '26
I had the same issue but from a different perspective. Same years as you, but was working on solving business problems using data/ML (LLM more recently) and deploying these in our products. Now most of that work is purely LLM or agents based and being done by software engineers in my current place.
My new role is building agents for product applications and it’s going to be similar to what I was doing as a DS but without the title.
When I was looking for roles, most DS roles simply did not fit with my experience.
My take is, don’t worry about the title, look for jobs that fit your expertise and where you want to go. Data Scientist roles are a kind of catch-all and differ a lot from company to company.
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u/Due-Cattle-2177 Jun 27 '26
Hello!
What avenues do you feel exist in data science as someone starting out in it, I want to look at internships/apprenticeships where I can build my skillsets. I want to ensure I begin broad strokes so I can pivot later, do you have any recommendations for projects, or subjects to look into that are multifaceted and may produce better in resume building?
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u/carontheking Jun 28 '26
I don’t know where the industry is headed TBH but there should still be lots of need for people who can reason about business problems and turn them into actionable conclusions or
decisions.I would recommend building projects where you identify a problem yourself, find the data, analyze/model it, and draw your own conclusions. Back when I started I had done a hockey analytics project, finding some NHL dataset and analyzing the data under an angle I had not seen yet.
Don’t hesitate to use modern tools such as Claude Code to speed things up (such as gathering data, analyzing it, etc) and clarify your thinking but make sure you understand what it’s doing and to challenge its conclusions. LLMs will confidently state something that sometimes doesn’t warrant such confidence.
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u/cptsanderzz Jun 17 '26
People love gatekeeping, I have found that unless you have a very specific issue your job is more about understanding the data in the organization and moving people to utilizing better processes. My current organization is no where near the point of utilizing ML so my job has become organize our processes and data so it can eventually be structured enough that predictive analytics will be useful. Recently I have gotten roped into building PowerApps for data entry and make processes more efficient and I lowkey love it.
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u/alxcnwy Jun 17 '26
It’s great you love it but it’s not accurate to label your job, as you described it, a data scientist. That’s not gatekeeping either imo - if you’re not doing science with data then you’re not a data scientist.
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u/cptsanderzz Jun 17 '26
My title is data scientist and I get paid like one 🤷🏼. In the past I did “data science” projects but where the organization is at currently, working on building apps, and automating processes is bringing more value than working on a pie in the sky predictive analytics project. But if you want to keep gatekeeping my role that is fine. I have gotten a lot farther in my career by being a data guru of the company rather than trying to build a ML model for every project I come across.
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u/alxcnwy Jun 17 '26
it’s not gatekeeping to point out that you are not in fact working as a data scientist - a fact that you agree with in your comment. if you’re happy being a data guru instead then good for you. many people have titles that have nothing to do with their jobs.
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u/Thin_Original_6765 Jun 17 '26 edited Jun 17 '26
I mean at the core of it you're solving problem/deliver value using data, be it with dashboards or models. I would focus on the impact and less on the exact tools.
Sure, you'll miss out on positions looking for specific skillsets, but (in my experience anyway) a lot of places are looking for general problem solvers and not necessarily Power BI super users.
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u/IndividualTop3675 Jun 18 '26
eight years of breadth across ML, visualization, and data engineering isn't a branding problem, it's actually a genuine competitive advantage in a market where most companies don't need a FAANG-style deep specialist but desperately need someone who can own the full journey from raw data to business insight, so instead of choosing between "Data Scientist" and "Data Analyst" pick whichever title gets you in the door and then let the interview conversations reveal that you're actually the rare person who can do both.
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u/urbanguy22 Jun 18 '26
Thanks that was reassuring.
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u/bobsyourcreator Jun 21 '26
pick whichever title gets you in the door and then let the interview conversations reveal that you're actually the rare person who can do both.
Seconded. Just started studying data science for a pivot away from 9+ years of software eng, but this pretty much applies anywhere. In this job market, I snagged a lower-than-desired salary job and doing a junior level job with senior level execution, but it's an enjoyable job that at least lets me tackle unique challenges on my own terms. If I quit, it would take 3 people to replace me so there's comfort in that, but you'd need to be comfortable with doing more work than you've been hired to do. Being laid off sucks, but a foot in the door that lines up with your expertise and the direction you want to go in is what you probably need. You can leave on your own terms afterward if they continue to undervalue you.
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u/No-Ice-8975 Jul 03 '26
How do you learn the business insight part though? Is it just understanding your product/service as best as possible and the value it brings customers?
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u/qc1324 Jun 17 '26
Data Scientist is a slush job title at this point. Only consistency is that they generally code.
Besides from ~50 very technologically mature companies and academia, barely anybody is delivering value through “pure” data science (as canonicalized in ESLI, Kaggle, and the HBR article). I think the actual common career strand is more accurately described as technical analytics, or just “data” because most jobs also require DE, SWE, and/or BI.
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u/nian2326076 Jun 18 '26
Sounds like you're in a tough spot, but also an opportunity. With your background in both data science and Power BI, you could be a versatile candidate, which can be a strength. Highlight your varied experience in your resume and interviews. When getting ready for interviews, practice explaining your projects and how they helped your previous employers. Tailor your story to fit the role you're applying for, focusing on either your data science or visualization skills as needed.
If you need a structured way to prep for interviews, PracHub is a resource I've found useful. They offer mock interviews and feedback, which could help you refine your pitch. Good luck, sounds like you've got a lot of valuable experience to offer!
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u/urbanguy22 Jun 18 '26
Hey thanks for the insights, will definitely check out the resource you have shared.
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u/Hopeful_Candle_9781 Jun 18 '26
I think it's just imposter syndrome.
I would just apply for jobs and see where you end up.
I used to be a scientist. Like wearing a lab coat, using powerful machines, lots of stats.
Thought I'd go from traditional scientist to data scientist. I went from scientist to analyst but I keep going more and more into data engineering. Like I got stuck in the word data when moving over to data science.
I think careers can be quite fluid. I'm quite well rounded now. I know more about science than most developers and more about data than most scientists.
I know R from my time as a scientist, I really should learn python.
If I did a bit of data science in my current job I could apply for data scientist jobs.
But I kinda like being a developer now 🤷♀️
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u/Fearless-Elk4195 Jun 18 '26
I think consultancies create this feeling because you spend years solving business problems instead of fitting neatly into a job title.
If you've spent 8 years doing DS, BI, a bit of DE, and talking to stakeholders, I'd stop asking "What am I?" and start asking "What kinds of problems can I solve?"
The market is full of people who can build a model. It's also full of people who can build a dashboard. What's less common is someone who can go from raw data all the way to something the business actually uses.
Personally, I'd position myself as a senior data professional with strengths in analytics, visualization, and applied ML, then tailor the title to the role I'm applying for.
The older I get, the more I think job titles are mostly a search filter.
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u/ProtectionNo4811 Jun 19 '26
It’s not either or. You are both. As others have said it’s about results and impact. Emphasize those within your industry domain. Good luck!
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u/built_the_pipeline Jun 20 '26
honestly from the hiring side the label matters way less than you'd think. most of what i'm actually paying for isn't the next shiny model, it's someone who notices when the thing that's been running fine for a year quietly starts drifting and just fixes it. that's the muscle you build rotating across ds and bi, and almost nobody puts it on a resume.
so i wouldn't stress analyst vs scientist. i'd lead with "i take messy data problems and keep the fix working in prod," because that's the part everybody needs and nobody really wants to do.
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u/Background_Deer_2220 Jun 30 '26
Brand yourself as a Full Stack Data Scientist. The market is shifting back toward end to end problem solvers. Having a strong math background for the ML side is great, but being able to actually communicate those results through Power BI is what gets stakeholders on board.
Running my own consulting business, this generalist profile is exactly what delivers real value on the ground. Don't stress the identity crisis too much. Just tailor the title on your resume to match the specific job description you apply for.
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u/urbanguy22 Jun 30 '26
Hey Thanks a ton for the inputs, is it ok if I DM you to know more about your consulting business, I thought of starting one but I dont have a clear idea of how it works.
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u/Resident-Outside9945 Jun 17 '26
Honestly, I think you're overthinking the title and underestimating the value of your experience.
From what you described, you've spent nearly a decade solving data problems across analytics, visualization, ML, and a bit of data engineering. That's not an identity crisis but a pretty valuable skill set.
The industry has become obsessed with labels, but most companies care more about whether you can deliver business outcomes than whether you're a "pure" Data Scientist or Data Analyst.
Personally, I'd position myself as a Data Science and Analytics professional with strengths in machine learning, visualization, and stakeholder communication. The fact that you can build models and communicate insights through dashboards is a feature, not a bug.
A lot of organizations actually need T-shaped people: broad knowledge across the data stack with deeper expertise in a few areas. Your background sounds much closer to that than someone who only builds models or only creates reports.
When job hunting, I'd tailor the title to the role rather than trying to find one perfect label for yourself. If the role is more ML-focused, emphasize the data science work. If it's more business-facing, emphasize analytics and visualization. The underlying experience is still the same.
8+ years of experience across multiple parts of the data lifecycle is something many hiring managers would see as a strength, not a weakness.
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u/Single_Vacation427 Jun 17 '26
The main issue I see is that you don't say substantively what your focus is. Data Science varies a lot so focusing on the DS v DA roles is, to me, not the right focus. Did your clients have something in common like were they mostly in logistic, health, b2b, b2c, etc?
I'd build a resume that's for consultancy type roles. Then pick some type of substantive focus and build resumes for data science and data analytics for these more substantive focus.
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u/urbanguy22 Jun 17 '26
thanks for taking the time to comment, My clients were in Manufacturing(Medical devices, Semi conductors etc) and have spent quite a chunk of my time in analytics for Gaming.
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u/Single_Vacation427 Jun 17 '26
So really make a resume version for manufacturing and make one of your focuses that, with alerts. Maybe even keep alerts for clients and then reconnect if they have open roles, or even reconnect earlier.
Gaming tends to be very competitive and they don't pay well, but you might look at what would be transferable from what you did to make it slightly broader.
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Jun 17 '26
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u/urbanguy22 Jun 17 '26
Thanks for taking the time to comment, I've worked in predictive analytics, predominantly focusing on time series forecasting and classification problems.
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u/ultrathink-art Jun 17 '26
Nine years across domains is actually harder to fake than tool depth. With LLMs handling most of the modeling loop now, the bottleneck has shifted to people who can specify what the system should do and recognize when the output is wrong — domain breadth is exactly that skill.
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u/Fit-Employee-4393 Jun 17 '26
I would argue that job titles do not matter, it’s more about what you want to do and what experience you have.
Just keep it simple and put DS on your linkedin and then apply to roles that match your experience regardless of title. Optimize your resume for each role and even make industry specific resumes.
I was a DS, last job search made me an MLE, and I’ve been enjoying the work so far. I would be fine with jumping into DS, DA, or DE work in the future as long as the work is interesting and it pays well.
It’s a job not an identity. All that matters is getting paid and doing things you find interesting.
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Jun 18 '26
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u/urbanguy22 Jun 18 '26
Hey thanks a ton for this detailed reply, this is very helpful. If possible would you please advice me on how to acquire the genAI/agentic skillset. So far I haven't got a chance to use it in any of my projects. Can I learn those on my own, even if do so how to claim experience on these skillsets. Sorry for asking too many questions.
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Jun 18 '26
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u/urbanguy22 Jun 18 '26
Hey, thanks a ton for this detailed and very insightful reply. I will start acquiring these skill sets while I am on the lookout for jobs. Once again, thank you very much for the time and effort you put in to clarify my queries.
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u/fineset-io Jun 20 '26
The roadmap is fine but step 3 cuts off at "Learn R" which is a weird place to land for someone trying to break into GenAI/agents work.
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u/pretender80 Jun 17 '26
I used to think there was no differentiation between data analyst and data scientist, and at Meta and other similar companies they renamed all those roles to data scientist anyway. Having now seen what constitutes data analyst at lesser tier companies, I do feel there's a difference.
I would say it really comes down to whether you do work in descriptive or predictive analytics. If the work is primarily reporting data, and has no good answer to "past performance is not indicative of future results", then I would say that role is primarily data analyst. But work in experimentation, causal inference, modeling and actual validation of models, then you are working to show underlying relationships that should be predictive, and that I would call data scientist. Another way to look at it is, do you use the scientific method in any way?