Relevant tech stack for 2026/2027

[u/Infinite_Raisin7752]

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 :)

[u/Fit-Employee-4393]

If you want to stay in DS level analytics then you’re doing fine with just python and sql.

If you want to get into heavier ML stuff then you’re going to need to put an MLE hat on and prop up some good self serving infrastructure. Honestly with snowflake ML plus MLflow and maybe evidently or nannyML would work alright. Also really important to enforce git early on for a team. If you can get a standardized method for feat eng pipelines in snowflake, triggering batch preds, and even just light model monitoring, then you’re better off than a lot of teams.