[INDIA] Advice needed for switching domain
I am currently working as an associate data scientist in Wells Fargo. Have just completed 8 months here. I(non CSE) am preparing for sde backend in product based company since last November. LC: 400(111/256/27) I have started applying and started giving few interviews for SDE role. But when I see my current TC and see other people having 18+ base with less yoe and having the same initial tc before switch (in my domain) it makes me feel so sad as I got low balled. I am getting calls from recruiter for product based company for data science role. Since I am focusing only on SDE ( for long term growth), I said no to them. I am thinking to apply and grind leetcode(mediocre coder) daily till I don't get the good opportunity. Question: Should I also apply for data science/MLE role? And give 1-2 weeks for revision as I am just out of touch since 8 months (did ML training before and have a good portfolio-github/Stackoverflow/hackerrank) in order to raise my salary by showing the offer and then stay in the same company since wlb is good and I will get time to study. (Risk: manager may increase the workload as I am getting so much time during ofc hour, currently working as a regex 🐒) If things didn't workout I will join the other firm and will prepare on weekends depending on the work life. #tech #SDE #dataanalyst #datascience #sde #machinelearning Yoe: 2.7 Tc: 🥜(12lpa (including pf, gratuity)+2(variable))
I am in the same boat. Wanna connect?
I'm a security engineer looking to get skilled up on ML. Why do you think SDE is better than MLE for long term growth? I certainly believe SDE is the safest option, but salary wise, I thought MLE would be the same on same levels, and perhaps grow much more in the future.
MLE role is good actually, requires ML+SWE. I said DS<<<SWE. Getting into MLE (proper MLE role) is quite difficult.
This is the second post I saw today, where a Data Scientist (or related) is planning to switch to SWE, reasoning similar to you. I wonder what makes everyone unsecured as a Data Scientist. And if such is the feeling, why not switch to an MLE role, rather than SWE. Anyway, you'd be preparing DSA for both.
MLE = SWE + ML You need to be good at dsa, ML system design with an experience of working in productizing the models and infrastructure. + Machine learning depth knowledge. Data science doesn't have proper role. You can be data analyst/data engineer/BI engineer under the hood of data science. For MLE they ask whether you have production level knowledge. In my org this part is done by swe(infrastructure). Getting MLE without experience in these things is very tough. One way is to become swe(backend) which has got good knowledge on dsa and system design and then convert to MLE. DS->SDE(backend)->MLE Applying directly would be very tough. With this much yr of experience it's difficult to move to mle position. I tried to move to production team and learn all these things but in banks we don't have much work related to this as compare to product based companies.
AFAIK, ML/DS roles in all good product based companies require corresponding SDE level DSA & Problem Solving. I would revise ML instead of preparing Web, Android, etc along with DSA/Leetcode prep and apply for MLE/DS/SWE-ML roles. Also, for many companies, SWE-ML or MLE roles are their core ML(research) roles.
I meant to focus on dsa(leetcode) and system design only. As for a good product based company they have multiple coding round followed by system design
DM if interested in MLE role
ML/DS also has very good scope as well. DM me for referral when you are ready for DS/ML roles
But DS role is not properly defined and interviews are very random can ask anything. What oracle interviews are based on ? More on stats(in depth) and sql? If yes then have to give more time to stats and maths behind each ml algo than the coding.
Not at Oracle any more