Is data science too easy everywhere?
It's my first job as a data scientist and the job is too, way too easy. I have been thriving by tweaking existing metrics, existing dashboards, existing queries, raise questions in data science slack channels, etc. Essentially, even if I had 0 knowledge of any data science responsibilities, I could pick up a project and ask questions in slack to make others build it while I'm just dragging and dropping and typing whatever the answers are... I understand it would be different where there's only a handful of data scientists or in a research environment of course. So,I guess the questions stands for orgs where a data science/analytics org exists. Tc:200k Loc: aus sydney
Thriving they say 🙄 Make a discovery that causes your product to hockey stick. Then come back.
What do you mean?
Google hockey stick growth. Then, make an insight or develop an algorithm that leads to a massive jump in a desirable user outcome.
You are doing Data Analytics. If you are translating business requirements to feature engineering, running complex sql queries to compute the features, do feature selection, then decide on an appropriate model, train, test, iterate etc., and finally tell a story, then you are doing data science.
I dont find it easy. I’ve been asked to put together models with like 50 samples and 20k features and labels confounded with other technical things. Very easy to train some overfitted garbage if you dont know what youre doing.
50 samples - lol .
its rough in biotech
To be blunt, the data science team at Atlassian just isn’t very good. The expectations are rock bottom.
Are you ds as well?
No, I work with them regularly and have done so at past companies as well
Sounds like you work at a company that doesn't know how to, or doesn't care to, leverage data scientists effectively. Your job sounds like an overpaid analyst. While this might sound great, over time it will cause your skills and abilities to stagnate, which will likely make it harder to find your next role...unless it is in a similarly braindead function.
Netflix, you can't infer about the org from one data point, lmao Have you forgotten CLT??
People can, and do, infer whatever they want from whatever they want. For example, I've never lived in New York City, but friends that live there and tell me about their lives certainly color my perception of the city. I have preconceived thoughts about how Atlassian functions in the data science space. This adds additional data to that previous knowledge.
does seem like what you're talking about has more to do with Business intelligence and data analytics. Lets put it this way, data science is the overarching umbrella, BI, Data Engineering, Data Analysis, ML are all sub-domains under data science. Seems like you're work revolves around some of the sub-domains.
I mean it kinda depends on what level you’re at right? I’m a DS as well and it’s absolutely not my experience that I could “thrive” by tweaking/dragging and dropping stuff. I’m measured on projects lead, new ideas/insights generated, product features developed and shipped etc. which is much harder than just maintaining stuff that already exists or jumping in halfway. So I don’t know if I’d agree that it’s a DS thing necessarily, it might just be a career growth/levels thing. Maybe your current level is below your capacity.
If you are doing dashboards, that is basically a glorified BI Analyst. Somewhere around 2017/2018 they started called BI analysts as Data Scientists and DS as ML engineer or ML scientists.
Hey Atlassian can you share some of the data science slack channels you frequent?
Sounds like you are doing Business Intelligence / Analytics and not Data Science.
What's the difference?
Data science is a vague term, and can either refer to analytics (think sql / dashboards) or building and developing ML models. The latter is closer to a ML scientist/engineer, and is where the fun and challenge lies imo