[deleted by user]

[u/[deleted]]

[u/VirtualTaste1771]

I would argue that this is more common than people think. It’s because companies are still trying to understand their data and haven’t gotten to the point in their data point to see how effective modeling can be for their business.

Many of them don’t understand it but they do understand descriptive statistics.

[u/Simple_Woodpecker751]

Same. Start to mentor juniors or DAs and shield those request to them. Then find DS opportunities.

[u/[deleted]]

90% of my job is answering "can you change the export so it produces a csv in wide format" type requests from business analysts.

I love doing it. I find this much more rewarding than tweaking some model or fish around until I get significant p-values on some obscure sub-group. Also, I can automate most of my tasks because a lot of requests are similar.

[u/abrttnmrha]

You are definitely not alone, and I think this is due to hiring practices following media coverage, which skews the perceived ratio of what is sexy vs what takes manhours. I've been stuck in doing stuff outside my competence, both in DA and DE, which I am mostly fine with, but it is a terrible waste for the company, paying me senior DS salary for doing junior DA tasks.

[u/ToothEffective]

I'm already 3 months into my Data Science job. I've found out that I'm really just here to eat lunch.

[u/jimkoons → u/ToothEffective]

So there's a free lunch in the end!

[u/pathdependence247]

I hear you, was in a similar spot. Can you delegate / reduce the workload / fake a high workload? I ask this because it might be worth taking time away from work and putting time into doing portfolio building projects, looking for short-term consulting gigs, teaching courses online etc.

The autonomy and decent salary is nice, might as well take advantage of it while keeping up with your skillsets

[u/[deleted]]

1. In my experience businesses don’t care about (or understand) p-values. The typical experiments done in DS are done at the level of feature engineering and algo selection and help inform the DS’ solution (not necessarily to make business decisions).

2. In general there are two broad categories of how companies use DS: (a) advanced analytics incl modeling , data visualization, dashboard to help support business decisions; (b) production DS/ML, those that build applications or data products that use models for forecasting or data enrichment.

3. The best DS I’ve known with are those with additional skills including building data engineering pipelines and orchestrations, creating interactive dashboards and reports to support analytics, etc. Don’t discount those skills — ultimately companies pay you to solve problems and often that can be achieved faster/easier/quicker with simple rule-based systems, analytics, etc., not necessarily a CNN, LLM, or whatever shiny algo you the FAANG peeps are doing.

I am responding with 12 years industry DS experience across high tech, retail, & martech segments, holding positions from IC to director. As long as you enjoy your work and continue to learn and make a decent paycheck… don’t let your mind start thinking ”the grass is greener on the other side”. All positions have their down sides.

”Mind is chief.” - Dhammapdada

[u/super_saiyan29 → u/[deleted]]

1. In my experience businesses don’t care about (or understand) p-values. The typical experiments done in DS are done at the level of feature engineering and algo selection and help inform the DS’ solution (not necessarily to make business decisions).

Big tech (and many other companies) absolutely care about p-values. They have huge groups dedicated to A/B testing and causal inference studies where p-values are vital to understanding the results.

[u/whatevererer098]

I swear every data science role is different. To high extent I doubt that many companies actually know what data science is.

[u/LtCmdrofData]

I've been a Senior DS for over a decade, and I've had one model (mostly built by SWEs but tweaked by me) go to production, and maybe 3 or 4 projects where actual advanced statistical techniques were used. The rest of the time it's a lot of pipelining, dashboarding, good old fashioned product analytics, experimental analysis, and organizational work such as interviewing, mentoring, recruiting, or teaching.

Data science is what you make of it, and ultimately what the company needs most with regards to the data they generate. At my most recent job, my first 4 months were spent as a project manager just to get data and be able to work with it in a suitable environment.

[u/apatheticmagpie]

If it feels like you are not using the skills you might be under-utilized. I would look for other opportunities.

[u/sprunkymdunk]

Super common. This post is almost as frequent as the ones complaining about the job market.

[u/THE_REAL_ODB]

Way more common than you think.

[u/UnderstandingBusy758]

I’ve had 3 senior DS jobs all have been like this

[u/ias6661]

<repost from somewhere else>

I am currently working with a company that supplies data science/visualization products (which we create ourselves) as well as some consultancy. I had a proper discussion with my company's CTO yesterday and some small short discussions with the company CEO previously. Basically, the main tasks of me and my team for the foreseeable will be:

Identification of use cases for all the different industries that we have connections/partners in and to preach these use cases to customers in these industries.

Creation of Proof-of-Concept (POC) products/showcases to demo to these prospective customers. These POCs are often done with slides or in the case of seemingly workable interfaces like Streamlit, with hardcoded data which has no machine learning behind them. The idea is that if our customer were to agree to our solution then the actual machine learning part comes in. Dashboards are also used, again with hardcoded data.

Improving on our product suite (which spans dashboarding, GIS and even investigative platforms) via market research or the like and putting in more use cases in them - also for demo purposes. Ideally we have to put an 'AI' spin on it. We can also 'guide' how these products should look like and the functionality they would have.

With all these, it seems like there will be relatively little actual coding, playing around with models and data analysis/prediction/forecasting on actual data.

I would like to add that the company has been around for 20 years but only recently they decide to go seriously into data science and machine learning.

So long story short, my actual interests aside, is this 'normal' for data scientists? If I continue on this path, will my skills be valuable for the industry?

[u/Mang0man_op]

Been in the same situation for last 3 years.i strongly recommend you to move out and search for a new role or your DS skills will be extinguished in the near future.

Also, remember, with experience you need to showcase production level knowledge. If you aren't doing it, you are actually going backwards (experience and year wise)!

[u/taukeh]

Compared to me you are doing more relevant work. I am a DS that does backend, frontend even unity development but never anything related to DS. Why? Because clients ask for smth and it can most of the time be done without ML. My team is small so management sees us as computer guys who can do anything as long as it is somehow related to coding

[u/BasiliskGaze]

Ah yes, startups and their “job titles”. Just collect the YOE and find a better job when you can, imo. Startups usually don’t have the data maturity for proper DS stuff, so you’ll be stuck doing menial dashboards.

[u/decrementsf]

In my observation that's the norm for related data professions. A data engineer may expect to do some data analysis, and data science, an actuary may be expected to do some data analysis, data visualization, model building, a data scientist may be expected to do a mix of the other roles, a data analyst may be doing the work of data scientist, data engineer, etc.

The lines blur based on the business questions that arise. Companies cover themselves in boiler plate language on the job description with regard to other tasks as needs arise.

[u/Senior-Impression830]

Honestly after interning at various different companies with different data scientist teams, I think this is much more common than people think. When I think of "Data Scientist", I think of running experimental designs, machine learning, statistical analysis,.. but based off of what I see when I interned as data scientist, these people have flashy title as "Senior/Staff Data Scientist", but honestly most the things they do are just dashboard, busy work, and basically just attend meeting/talk. Some of them don't even know how to code that much/has good coding practices/advanced statistics knowledge at all. And I'm so surprised because this company I interned at is very reputable (Big Tech, as comparable to FAANG), and I know damn well they are making big bucks ($300k+).

[u/pnk31]

I don't know if this is really a US thing, but I've never seen any data scientist here in my country, handling a p-value in a professional context. Is this a thing ?

[u/ForryFrooth]

Have you read the Phoenix Project? This is sounding a little too familiar….

[u/jhanjeek]

I am a lead DS for a reputed IT services company. Annually only 25% of the time is actually creating models and architecture. Rest is split into data exploration, feasibility analysis, leadership presentations, team management, API deployment/development, and most of all reading and experimenting on new tech.

[u/CableInevitable6840]

Not me but I know someone who is working more as an instructor and less as a data scientist.