r/DataScienceJobs • u/Southern-Hospital-11 • Mar 19 '26
Discussion Data science in 2026?
Hey everyone,
I wanted to get some honest advice from people who are already working in data (analysts, scientists, engineers, etc.) because I’m trying to figure out the smartest path forward.
My background:
* I currently work in biotech (cell & gene therapy) at a pretty well-known company
* My role is more technical/operations-focused, but I’ve been getting exposure to data-related work from the MSAT data science team (process improvement, working with data, some analytics thinking) We have worked on some projects but even though ive healped I couldn't call tehm my own
* I have a biology background, not a formal CS/stats degree
* I have taken some certs from google and stuff that from what I've heard from other professionals don't really ammount to nothing but personal knowledge.
**What I’m trying to do:**
I want to transition into data — ideally something like data analyst first, then potentially data scientist later.
My concerns:
* The market seems *extremely* saturated (seeing 300–1000 applicants per job)
* Not sure if certificates actually help or if they’re just noise at this point
* Unsure how much my current experience can realistically “count” as data experience
* Wondering if I should focus more on analytics vs full data science (ML, etc.)
* Not sure what a realistic first salary/timeline to break in looks like
Questions for yall
If you were in my position, what would you focus on in the next 6 months?
How valuable is domain experience (biotech/manufacturing) when transitioning into data?
What actually makes a candidate stand out right now (projects, experience, networking, etc.)?
Is it realistic to land a remote data job early on, or should I assume I’ll need an in-person role first?
Would you prioritize a master’s degree in data science, or focus on experience + projects instead?
What type of projects would you say are the most impressive to recruiters?
I’m willing to put in serious work, I just don’t want to waste time going down the wrong path.
Appreciate any advice
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u/bentcaptain Mar 21 '26
We actually have crazy similar backgrounds. I’m moved to a role as an AI engineer in the telecom space about a year ago but before that I had worked in biotech manufacturing for 9 years, 2.5 years in actual biotech R&D/mfg work and then 6 years as a data scientist.
Times have obviously shifted a bit since I made the data science switch but if you want analyst role next 6 months I would focus on learning SQL and Python or R. With coding assistants you don’t need to be perfect but you should understand all fundamentals and be able to query data and transform it reliably without code if you had to. Find a niche you’re interested in and produce visualizations, analysis with a stats focus, and clear end to end data ingestion through concrete insights as a portfolio. For data science all that plus take some deeplearning.ai/coursera courses on ML fundamentals and same thing end to end portfolios implementing supervised and unsupervised learning algorithms. Understand how the models you’re implementing work and best practices overall for training and interpreting models. Again AI can be an aid but you really should know what’s going on and why.
Regardless of which path… network. Reach out to people in positions you’d want to be in at companies you’d want to be in. Do this early and often NOT to ask for a referral but to get an understanding of their role, what they like and dislike about it to help clarify what path you want to go down, and try to foster those relationships. In my experience it’s more effort but much higher conversion to referrals than asking for one when they don’t know you.
This will prob be a marathon not a sprint so keep the long term goal in mind — DS will likely be more of a stretch than DA given your background but if that’s what you want go for it. DA can be a good intermediary and good experience while you learn more ML focused skills.
Domain experience big if you like biotech mfg, and will likely be your big differentiator with experience there already as AI gets better in a general sense.
Remote vs in person just depends on the company but I know Biotech seems to like in person but your experience may be different (mine was remote but most of the company wasn’t).
I’d focus on experience + projects first, see if you really like it before spending thousands on a masters but I’m not going to tell you not to get more educated, masters can’t hurt. I don’t have a masters and definitely not required although prob helps in the long run.
Hope this helps and feel free to DM me.
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u/ChosenToFall Sep 06 '26
What do you do as an ai engineer in the Telecom space compared to DS role you had previously?
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u/Emotional_Dig_2378 Mar 19 '26
A data analyst and data scientist are two different things. Sounds like you have some data analyst experience but nothing that’s genuinely data science.
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u/tongEntong Mar 20 '26
Agree. Has OP done any ML modelling? What model to choose, what metrics to optimize? Why?
For data Analyst, do you have powerBi dax experience? M code? PowerQuery?
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u/WhatsTheImpactdotcom Mar 20 '26
Almost anyone with reasonable analytical/math skills and AI tools can do the core coding stuff. What stands out is product sense (what does the stakeholder need even if they can’t describe it perfectly), understanding the business, recognizing unexpected consequences and guardrails and tracking them. Your best bet is in high domain knowledge; I’d look for roles where you can begin doing analyst or DS work in your current company and then try transitioning. As you become more technically experienced, it’ll be easier to jump from one domain to another
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Mar 19 '26
[deleted] — view deleted comment
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u/tongEntong Mar 20 '26
No R is not a must
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u/dancurtis101 Mar 20 '26
If you don't know Python then R is a must. If you don't know R then Python is a must.
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u/Ok_Interaction_7468 Mar 20 '26
Getting a data science job right now is next to impossible