It's almost my one year anniversary in MSFT (joined Oct 2018). I'm giving back to Blind, so AMA. First, I have a Non-Trad PhD (aka not CS or maths). I made a mistake of not following TC. Wasted 5 years of my life for a useless PhD, and condemned to endless postdoc in the hope of shitty professor position (TC $100k). I have 15 papers from grad sch, 9 first author ones. 3-5x more than the average grad student. My tip for the academia cult: NOBODY GIVES A FUCK about your stupid papers (maybe unless it's a relevant PhD?). In my 4th year of grad school I woke up, but it was too late to change. I grad in 5th year and did 2 yrs postdoc (TC $46k) because I had no fucking options when looking for a job. Even though I was superstar, I didn't matter. Life outside tech is PURE SHIT. During this time 'data science' and 'big data' was hot. For the next 2 yrs, I chased it too, but chased deep learning (aka 'AI') as I thought it would be the next big thing. I also stopped publishing in journals. Fuck papers, they weren't getting me what I wanted. Fast forward to today, I got promoted to L63 and have 140% target bonus. Was hired as L62 at MSFT last year. In 2016, I had 1 onsite and 1 offer. Fuck this! In 2018, I had 10+ onsites and 6 offers! My highest offer was AMZN L5 at $240k. But I took MSFT. My TC is now $220k. Not too bad but could be better. Current YOE is 5yrs PhD, 2yrs postdoc, 1yr at MSFT. I feel very liberated now that I am outside the ivory tower. I feel fucking loved. For those who want out, I know the feeling. It can be done. AMA.
I’m in a similar situation but with 1/2 the number of publications you had. 1. How important is LC? How much did u LC? 2. What kind of knowledge did u have to learn to qualify for the ML role 3. I took a DS class in college and is trying to refresh myself on it by studying more on DS. Like the usual 10 algorithms of supervised and unsupervised learning, EDA, cleaning dirty data, cross validation. Is this going towards the right direction for ML? Or is this for DS? 4. How important are side projects? 5. Was your ML knowledge self studied or did you take a boot camp or classes
I am in a similar situation, and I would love to hear from OP.
1. I did not LC enough. Just start LC now that's my advice to you. For my role I felt that LC was 50% of my assessment criteria. Maybe more. 2. I got a conference paper accepted to a ML conference, and a few ML workshop papers which helped improved my legitimacy. However see 1 for LC. Getting to ML conferences is only good for networking and picking up recruiters. Once to get to onsite nobody gives a shit. Out of 10+ onsites only one asked me to present my work. 3. Yes that is more ML than DS. You want to avoid stuff like tableau, powerBI, excel and to some extent SQL. A SQL-heavy job will not be ML focus. You will need to study like a college student because you will meet a llot of 'idiot interviewers'. So treat it like an exam. My literal advice is just memorize shit about ML, how the algos work, etc. I failed a few interviews because they kept asking me simple questions like these that I would normally look up. You may have to regress a little from your research mentality to cramming for exam mentality. 4. My ML papers were my 'side project'. I basically applied ML in my domain speciality and hammered ML/CS conferences hard. 5. Self studied. You have a PhD or close to one just learn it yourself. It's not hard. You just need to learn a new field.
What's your PhD in?
Do you think a graduate degree is truly necessary for a lot of the DS/ML roles out there or is it just a filter to get rid of those who only hold a bachelors? Also, would you mind sharing the interview questions you got in 2018 and your prep?
Yes and no. Unless you're researching new algos (and that's only DeepMind, Google Brain, MSR and FAIR), a PhD is not needed. The diff between BS and Masters is non existent. If you have BS and can get into FAANG go for that. Masters IMO is letting you roll the dice again. I'm not sure if not having a Masters will be a technical issue like some company requirement for promotion in the long term. The PhD does help with solving problems. Lots of time SWEs and new ML engineers ask me how to debug ML stuff when it doesn't 'work'. I don't know to explain but it's easy for me to troublshoot. I suppose after 7yrs+ of working through problems it's some sort of training, but any competent person can learn over the years too. In the 10+ companies I had onsite with and probably 20+ phone screens, there is no consistent pattern in their questions unlike leetcode. In some sense, it makes it more difficult because the scope is so big. Also there were just so many questions but I would be confident that 70% were a variation of 'explain X' or 'how does X work'. Unfortunately X can be stats, probability, ML, DL (if you apply for DL positions). Coding is leetcode easy to med, some DB/SQL too.
How many LC easy and how many LC medium did u practice during prep? Do you feel it is necessary to also spend time on LC hard? How comfortable were you with LC easy and LC medium?
What primary languages do you use? What primary type of modeling do you do? Do you put anything into production?
Python. No R. Mostly ML and DL. Not before I joined MSFT.
Yawn
I am in tech but would like to get into bioinformatics. Doing an online masters. I suppose you would tell me to turn back now? :) Do you ever feel you could be more impactful in bioinformatics than at msft?
Where are you doing online masters from ? Also how is the pay in bioinformatics?
Bioinformatics is shit. Pay is shit. WHY would you leave tech? This is the most real advise you will get. Anything bio related even biotech and bioinformatics is shit in terms of TC, and career outlook
Hey Op.. mind if I dm ?
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Thanks for sharing your experience, and congratulations! How was your experience at MSFT? What would you suggest if I want to move from DS to ML scientist?
I love MSFT! Great team, great TC! My experience is limited to those who are coming from a non CS research background. I would definitely avoid at all costs to be branded as a 'DS' (rebranded data analyst, business intelligence).
Do you feel that taking on that title alone gets you the branding? Would you suggest to only look for ML titled positions?