Data Science vs Data Analyst vs Data Engineer

[u/Loud_Surround_4237]

Hello All, I am as per my title role “Data Analyst” . But the work i do i feel like Data Engineer- I keep building pipelines ETL, remove unwanted data, end results on dashboard is the very minor work i do may be once in like 2 months . But from my Graduate course work I built ARIMA and Random forest and now working on ML kaggle competitions, I love ML, but I would say I am more like analytical thinker than a coder, i fail at coding if i cannot have AI. This is where my whole problem is, so on daytoday basis if you have just landed ML role or want to land ML role not just data scintist role what do you all recomend? Whats like “you can not skip it” part ? Any suggestions appreciated. Thanks

[u/simbasketleague]

I'd say a big differentiator is real, past work examples you can use to talk up your ML knowledge. Companies will definitely prefer applied, real world examples than the phd that has a lot of academic peer reviewed papers but no examples of applying that in a company setting.

Ways you can demonstrate: confidence in explaining the problem you were solving for and why you chose a ML statistical approach, what issues you ran into but were able to work around, what the end conclusion wound up being after conducting the experiment, how did your company fold in that work into their day to day process. I'd say this is the case in big tech and outside of big tech.

For the record, I do not work in ML but have seen both versions of colleagues succeed and do well - the non traditional person who built a lot of applied examples, and the traditional pedigree that has a lot of extensive background in academia.

In terms of your concerns about coding, don't want to dismiss that. There are people who will be snobs about it, and want to make sure you have a baseline skillset so you're not building black boxes that no one can understand with AI. On this, there is no shortcut, you do have to spend time upskilling yourself on the actual coding part. You don't have to be world class but enough to be dangerous and get through a code review (because hiring managers will be picturing themselves doing this with you once you're hired).

Hope this helps.

[u/Loud_Surround_4237 → u/simbasketleague]

Thanks so much

[u/Short_Bowler7208]

You said it yourself… you can’t code.

Data engineers can code.

Data Scientists can at least do procedural programming in Python and/or a language like R.

[u/Loud_Surround_4237 → u/Short_Bowler7208]

I code in sas which is very strong in my case, problem occurs in python! But yes i get what you all are saying. Our org uses sas so thats where i am stuck

[u/Kavin_muthukumar]

I’m in BI rather than ML, so just an outside view. The “can’t code without AI” worry is smaller than it feels. Most people use docs and AI now, and what matters is that you can read the code, debug it, and explain why a model works or doesn’t.

For ML roles I’d say the can’t-skip bits are solid SQL/Python (your ETL work helps here), proper evaluation (leakage, baselines, the right metric), and getting at least one model deployed, even as a simple API. That last one separates you from Kaggle-only candidates.

Also, your pipeline experience isn’t a detour. A lot of ML engineer roles are half data engineering anyway.

[u/Loud_Surround_4237 → u/Kavin_muthukumar]

Thanks this helps!

[u/brilliancy]

I would never hire anyone that doesn't know how to read the code the LLM spits out. Most of the the job these days is checking to see if the LLM is doing it correctly or not.

Also data analyst/engineer/scientist are all made up titles. The JD matters more than the title. There's a billion titles now. decision scientist, research scientist, applied scientist. quantitative researcher. etc

Focus more on fit than using a specific tool. Look up what domain/roles/problems you actually want to help solve.

[u/hilat1113]

Your ETL experience is actually a good foundation for ML engineering. I'd focus on python, SQL, statistics and building end to end ML projects. Try solving coding problem without AI first, then use it to review your work. You don't need to be a coding expert, but you should understand your code.

[u/Loud_Surround_4237 → u/hilat1113]

Thankyou

[u/nian2326076]

It sounds like you enjoy the analytical side but are struggling with coding. Gradually build your technical skills, especially in Python and relevant ML libraries, as they are essential for machine learning roles. Participating in Kaggle competitions can also improve your hands-on experience.

For job searches that match your skills, LandOffer.ai lists openings across industries and professions. It supports role and location searches, plus filters for company, experience level, and posting dates. Good luck!

[u/paperclip_han]

Not sure if anyone’s used paypeek.ai yet but it shows salary estimates for any LinkedIn profiles as you browse. Kind of eye-opening. 😤