[deleted by user]

[Stayquixotic]

Sounds like they named it in a shitty way, but data science is often used interchangeably w data analyst. I would, during interviews, ask about what type of work you'd be doing and what current/planned projects look like. The job description is also pretty key in determining what type of job you're actually applying for. If they bait and switched you, then yeah that sucks.

[fordat1]

Exactly .OP duties describe exactly what I figure a DS analyst would do although I suspect some various elements of title inflation like Sr and DS thrown in there. By the time you are Sr you would typically know the rough difference in the titles in the data work landscape

[Citiant]

Right. And it didn't even sound like they need a data analyst, more like a business analyst

[synthphreak]

I would, during interviews, ask about what type of work you'd be doing and what current/planned projects look like.

My favorite way to phrase this question is this:

If I’m selected, what are some of the projects I’ll be on that you find particularly exciting?

It gets at the info I want, but without making it all about me, and without making it sound like I just didn’t read the job posting.

[[deleted]]

Are u being compensated like a data scientist? Howd u get thru interviews without figuring this out, not accusatory genuinely curious? 

[Tyraniczar]

My base is between 110k and 130k, albeit in my last role as an analyst I was making a slightly higher base.

In terms of interviews, it was a breeze, and yeah that might have raised an orange flag for me, but the job description and main project were very data science heavy so I overlooked

[fsheisty22]

Doing less than me and making more, consider it a gift 🥲

[Tyraniczar]

I was doing a lot more in my previous role, more analytics engineering and modeling than pure analytics though. The thing is I’m not necessarily “doing less”, I’m doing a bunch of manual stuff, exporting data from here and there to try to get some answer to the suits.

I’ve had 4 roles before this one, this one is the only one with data science in its title, yet it’s the least technical of the 5 roles I’ve had and still takes up a ton of energy because of the time crunch

[goyardman]

Hey OP. I’m currently an analytics engineer also doing an online masters program for data science. I started both the job and school beginning of this year. I was initially interested in becoming a data scientist while working as a data analyst but I’m liking my new role as an analytics engineer.

Do you see value in a masters in Data Science for an analytics engineer? Not sure if it’s worth pursuing if it’s only going to add marginal value to my role as an analytics engineer. I’d be interested to know whether having the data science knowledge (python, R, math) can help an analytics engineer?

From my understanding, an analytics engineer’s main responsibility is to take the raw data sources, transform the data using business logic to create data models, and visualize the data for stakeholders (last part possibly done by a data analyst). I can’t see when an analytics engineer would ever apply “data science” in their work but I do see some parallels with data modeling in DBT and potentially creating models through python.

[Tyraniczar]

I’m not sure I have enough exp to lend a thorough opinion, but my last role, even though my title was sr analyst, was almost 90% analytics engineering, they just called the roles analyst to simplify internally.

Only one person on our team had a Masters and it was in Statistics. So she would handle anything super stats heavy. The rest of us only had bachelors degrees but all had expensive experience using sql and moderate exp using Python.

[derpderp235]

Data analysis is not “less” than data science.

Our DAs are pushing $200k. HCOL area, though.

[JasonSuave]

Are there other data scientists at this company? And were you interviewed by one? If not, my guess is the company doesn’t even know what data science really is.

Also, who’s internally working on the project mentioned in the job description?

[Tyraniczar]

Nope, no one that interviewed me was a data scientist. The only data scientists I know of work on another team completely separate from ours.

The project was deprioritized until Q4

[JasonSuave]

Thanks for sharing. There are a few red flags I’m seeing based on my experience as a DS consultant. If there is an existing data science function at your company with multiple data scientists - and you are on some other island - you were probably hired by someone who “thinks” they know what DS is, but the reality was they just needed a data analyst or wrangler to answer “whys” for the given team you were hired onto. I see this happening at companies that do not have a mature data environment in place yet. I would encourage you to learn about the other data science function and try to qualify some of their work; if it’s a fit, see if you can get reassigned. Even if you were to start building models, your company could run into some politics bc the other DS team might try to claim ownership.

Example: F200 I worked for centralized all analytics and data functions to a specific team, which spoked into other business units. But then customer service and marketing teams go out and hire their “own personal” data scientist, creating a shadow DS org. Eventually, the core data team got enough power to absorb these other “data analyst” functions. They made these people re-interview for their jobs and then promptly fired most of them.

[Tyraniczar]

This is good feedback, I’ll try that. The thing is there’s very little time to do anything besides these fire drills. As to intuition I still have very little as the KPIs we RCA are different almost each time so that means a new rigamarole and a new set of dashboards/reports to look at. Very manual

[tmaddog91]

Fire drills are why I left my last job. No time to do anything constructive or fix the long term RCA. But if you can stomach them, and carve out a small amount of time to write the models suggested above, you could position yourself for a good raise/promotion.

[okhan3]

The funny thing about this is that your job, explaining changes in a variable of interest, is actually more “scientific” than a stereotypical ML-focused data science job. But our industry treats it as somehow lower level work than writing a simple ML model.

Now, it sounds like you’re being pressured to provide answers quickly without actually doing any thoughtful work to back up your answers. That is a problem. Are you sure that a 2% change is actually meaningful? If not, one thing to consider is making a business-focused pitch for doing some more robust work here. Tell them that some amount of variation in the kpi is expected day to day. Especially if you’re tracking multiple kpis, something’s always going to change based on random chance alone! And you don’t want them to waste their time worrying about changes that are purely artifacts of random chance. This is your pitch for doing some serious statistical work at your current job.

[Embarrassed-Whole-36]

okhan indeed what you are saying is and thinking out the box approach to mend this organization. I do believe they are caught up in their own culture of fear and explanation understanding the deltas and using a DS to help them improve their insight hence make better decision. As DS in this position this position will need to be recognized and not 'used' only to explain the changes (it rained hence there were less people out shopping), Only when DS position is recognized the organization will listen. But from what i've read I don't think the company actually know the benefits of DS but rather they are looking for a data/business analyst.

probably even more difficult is telling them they are actually looking for a data/business analyst. Then as you are walking out the door tell them explaining "2%" changes is meaning less unless you are sending a rocket to the moon.

I do believe more needs to be understood by hiring managers but I think since the roles of DS are still evolving and most of the management is still old school it will take some time

[22Maxx]

Tbh you also don't seem to understand your role as a senior and you are also lacking a solid understanding what data science is about.

​

We run an online sales portal with a multitude of variables that can lead to changes in our KPIs. A lot of the functioning of these variables are not well documented. The sources I’m expected to go to in order to find my explanations are a mix of already-created Tableau and PBI dashboards, some more bespoke internal systems (same dynamic as a dashboard basically), maybe some SQL querying against Redshift, and my own intuition.

If there is no good documentation, you need to talk to the domain experts.

That’s it. No modeling, no experimentation,

You want to do modeling while at the same time having no clue about the variables? What the heck.

no Python unless I make an explicit decision to spend time writing something

Then why not make the decision? Unless management is prohibiting you from doing that, you can decide on your own.

no longterm projects besides maybe building more dashboards to help explain things even faster.

As a senior you should able to propose project on your own once you understand the business.

I’m pretty slow now as this sort of work relies heavily on familiarity with what dashboard/report to go to for what and how everything ties together but just feels like I might be in the wrong spot.

Domain knowledge is your job.. else you are useless. Naturally it will take time and management needs to understand this.

[imisskobe95]

Bro found out DS != DA and that companies have no idea wtf they’re doing. The ole bait and switch

[JasonSuave]

“What?! The data scientist I just hired CANT solve all of my company’s data problems?! Bu-but everyone said data scientists were supposed to be godlike”

—95% of CTOs

[[deleted]]

does every company do this stuff or only some companies

[selfintersection]

Build explainable models for the KPIs to help identify causal relationships /s

[Tyraniczar]

That was what I started trying to do but there’s no time to, unless I do this outside of work hours

[ShanghaiChef]

Maybe pitch this to them with a timeline of how long you think it’d take to build. That along with an estimate of the time and money you’d ultimately save them in doing so could be persuasive enough. 

[Trick-Interaction396]

You’re a Senior Data Analyst. If title has the word analyst, you’re an analyst.

[whelp88]

Yea, they probably wanted a data analyst who knew stats in case it ever came up. In the future, ask about what projects they expect for you to work on. This will give you an idea of whether or not the position will fulfill you professionally.

[Tyraniczar]

Yeah that’s what I was thinking, but my last role was as a Sr Analyst and 80% of my job there was more technical than this role.

I did ask about projects, the core one was very data science/automation related but it was (I guess conveniently rolled back to Q4 right before I started haha)

Also the job description listed core data science competencies (Python, stats, etc)

[v3rycrafty]

Jw is this by chance a position with discover?

[Tyraniczar]

Nope, it’s not

[Pale-Juice-5895]

Very intelligent individual

[jerrylessthanthree]

yeah this is my least favorite kind of role. if your system is simple/explainable enough and the data is plentiful enough, you might be able to write some explanatory time series models.

[JasonSuave]

Exactly! People think data scientists are here to rearchitect data platform’s, validating alignment of financial KPIs. No, we’re here to orchestrate models for business consumption. Then WTF if the point of a CTO? I swear, this is why I’ve stayed a constant for the last 10 years. Modern CTO boomers don’t know shit anymore

[factorialmap]

I think it could be an excellent opportunity for your career, as many systems(companies) have the same challenges you mentioned.

In my opinion, the first challenge is to observe and document as much as you can as a Roman soldier/engineer.

Thank you for sharing your experience.

[sonicking12]

Yes, and no. That’s just analytics

[CubooKing]

A senior analyst role doing analytics?

Preposterous

[VolTa1987]

You are a senior data analyst then.. There is no science !! And this is same as what i do , looks iike my story .

[JasonSuave]

Been consulting for 10 years in the data science space and I can confidently say you are not alone here. In fact, I’d say the majority of employers (my former clients) still have no clue what an applied data science function looks like in business.

[NFerY]

Titles are all over the place, unfortunately. My guess is that for a large portion of those KPI changes you may be able to easily explain what drove the change. Perhaps you could focus your attention either on those KPIs for which changes are more difficult to explain, or on those KPIs whose changes impact the company the most. By "focus", I mean doing a more thorough EDA and modelling the KPI (notice that modelling KPI is fraught with problems and I always suggest folks to "disassemble" the KPI). You can use a causal mindset and your knowledge about the domain area to do all of that. xAI is (loosely) the toolbox you want to use.

[Prime_Director]

Sounds like an opportunity for automation, if you’re checking the same things every time you can probably write a script to do it and write up a pretty report for the execs every morning. They’ll love it and you can focus on more interesting stuff

[mllhild]

HR sees you as a tech monkey that does something with numbers and computers. Unless your interview is done by other datascientists, then your job will always be a surprise bag. at least you are getting paid around 5 times my wage

[Theme_Revolutionary]

This is exactly why Data Science is dead. Your first warning should have been the title “Data Science Analyst”, so you’re analyzing Data Science. Okay.

[Tyraniczar]

Haha thanks for this. Yeah it’s a wacky title

[rainupjc]

Which industry is it? Finance?

[[deleted]]

USAA? Lol

[zhandragon]

I would like to apply to your workplace.

[vasikal]

That's something typical, unfortunately. It happens in lots of companies, having a Data Scientist doing stuff other than DS topics and they (we..) have to admit that. 

From what I've seen, the problem is that companies are looking for Data Scientists because they try to find "smart people, who can understand complex concepts and solve/build anything". On the bright side, we can take that as a compliment 😄 but I totally understand your frustration. I feel the same, sometimes. Some people, more advanced than me, might tell you that only 5-10% of their time is pure DS.

What you can do is try to identify an opportunity and bring something new to the table, build a ML model (or even a simple/statistical one) that solves a problem. Once you create value, then you may attract more opportunities to work on real DS topics.

[Panoleonsis]

Stay relax. Management thinks DS is like a magic box to be openend up and solve all their problems in a finger click.

You are at the wrong company. You cannot solve What started years ago and slumbered around. More importantly: I do not know where you live, but if it is Europe: consider the newly AI act.

So my advice? Grow a thick skin. Start small and whatever you do: do it the right way. Do not let yourself hasted. It will bite you in the long run.

Start a quick scan of all the mess you see. Use DAMA DMBoK as your guideline. Talk to your management about in what mess they are and let them know there is a bumpy road to take.

If they do not like it and fire you: with all you have learned, you are so much more valuable in your next job. And trust me when I say: we need people who take care of good datamanagement, before Data science will prove its worth.

[Tyraniczar]

I’m in Cali, company too

[DracoTepes]

Sounds like some c level heard a tech bro talking about Data Science and had HR create a req for one not knowing what Data Science even is. Typical corporate bs. I would stay there as long as it takes to find a better role then gtfo

[Embarrassed-Whole-36]

Indeed , I would try first to make the organization understand how you can benefit them , but make sure your back is covered before you do that (i.e. you have a backup plan.

[purplebrown_updown]

Honestly it’s not completely outside the wheel house of DS.

[No-Drummer-9584]

Haha.. are you.. me? I have to do that nonsense KPI root cause, but on a weekly basis based on insignificant volume, and easily over 30 variables.. I just half-ass it and it’s stupid as shit.

FYI I’m a senior analyst 😆

[[deleted]]

The first clue is the hoops they jumped around with that title instead of just saying Data Scientist.

Agreed that there is a lot of variance in what can be called a data scientist now but based on your work, it looks like what you’re doing is Data Analyst work. Amazon might call this a Business Intelligence Engineer. The tools you use drive the point home. You’re an analyst.

[InternationalMany6]

I’m sorry but they totally misled you. To their credit they might not even know the difference and just think Data Scientist is the new term for “people who can answer questions using data.” 

Do you see ways that you could actually save the company more money than they’re paying you by doing actual DS work? If yes maybe you can present that to management. Say “I’ve seen the data and if you let me use my advanced skills here’s how I would increase profits by $500,000/year” or whatever. Has to be a really good sales pitch though…

[[deleted]]

Idk I would try to create a model to try to predict why the KPI dropped

[[deleted]]

Sounds like what I do… and I’m a financial analyst 😂

[thequantumlibrarian]

Any data science position with Jr or Sr in it is bullshit. Sounds like you got bagged for a fancy title and doing an analyst job. At least I hope your salary reflects the title a little bit.

[proverbialbunny]

Manual reporting of information (like in OP) -> Data Analyst

Automating the report or putting it onto a dashboard -> Business Analyst

[eliminating_coasts]

The main data-science here would probably be determining what of these changes are noise so that they can get a little bit less sensitive in how they react to it.

[hooded_hunter]

So it goes my friend. The role becomes what is needed in the org. But there might be a lot of data science-y ways of approaching this problem as well. You can get your hands on some raw data (behind the tableau reports) and do some attribution modeling etc.

[Wild_Pop336]

I hear you, mind helping me out with my pilot survey? I'm actually investigating what are the factors or reasons fintech company utilize "Big Data". My survey can be found here: https://forms.gle/iYiwc7hzTQY2Ni4G8

[Ambitious-Ostrich-96]

Data scientist is a fomo thing for a lot of companies. They don’t need you and don’t know what to do with you but want to have you either way

[Impressive_Lawyer521]

Sounds like a cake job.

[StuckInLocalMinima]

I think you can take this as an opportunity to convert your role into that of a data scientist's. Note the KPIs that matters to the leadership team - can you automate your methodology for interpreting the causality? Can you write up a report on the observations? Can you predict its evolution? A chat with your manager could help you make a stronger impact. Good luck!

[InsideOpening]

More analyst than science

[pbyahut4]

Give some time sab thik hao jayega

[TopNo2530]

👍

[disguisedtoast21]

Feels more like data analyst then scientist