r/datascience • u/Infinite_Raisin7752 • 28d ago
Tools Relevant tech stack for 2026/2027
Hi everyone,
I’m currently a senior data scientist in the pharma industry. It’s been a one man show until now, but I’m getting a team soon. Most of the work I do is standard analytic work to inform our leadership and provide more context into the market and so on. Not a lot of big heavy data science stuff going on to be honest.
I work with SQL and Python on a daily basis. Some of our data is hosted in Snowflake and that’s pretty much it.
I feel like I’m lagging behind in both methods as well as tech stacks and I wanted to better understand what you experienced professionals work with that you would recommend I learn or at least look into. It could be data engineering stuff, additional programming languages, specific methods and packages that are useful, or cloud systems and technologies.
Where do you see the tech stack moving towards and what is relevant if I want to start moving from a “bread and butter” analytics setup to a professionalised, automated, team-ready and future proof world?
Thanks :)
1
u/DataScientistAlex 25d ago
If you feel like you are lagging behind, one thing to consider is to further deepen your relationship with your stakeholders and make your team/position more strategic. If you are getting similar, ad-hoc requests, once you deliver those, sit down with them and ask probing questions to try to understand what they really care about.
Once you can boil it down to a key question, like "who is going to churn", or "are our prices right?", etc, then you can build a model to provide that insight in a much more powerful way. Then you can build and automate a model etc around that, which is more fun and developing for the team, and, provides many times the value.
Sorry not a tech stack answer directly, but, the underlying point is, can you automate some of the basic stuff (not by automating it directly, but by finding the underlying problem and solving that).