•1 min read•from Data Science
What's you recommendation to get interview ready again the fastest?
Our take
To get interview-ready quickly after losing your big tech data science job, focus on sharpening your problem-solving skills and cognitive abilities. While practical experience drives daily work, interviews often assess your raw analytical thinking. Consider dedicating time to solving logic puzzles and LeetCode challenges to enhance your technical acumen. However, don’t neglect the importance of understanding broader concepts and frameworks relevant to data science.
I'm not sure how to ask this question but I'll try my best
Recently lost my big tech DS job, and while working I was practicing and getting good at the one thing I was doing day to day at my job. What I mean is that they say they are interviewing to assess your general cognitive ability, but you don't actually develop your cognitive abilities on the job or really use your brain that much when trying to drive the revenue chart up and to the right. But DS/tech interviews are kind of this semi-IQ test trying to gauge what is the raw material you're brining to the team. I guess at the leadership and management levels it is different.
So working in DS requires a different skillset and mentality than interviewing and getting these roles.
What are your recommendations/advice for getting interview ready the quickest? Is it grinding leetcode/logic puzzels or do you have some secret sauce to share?
Thanks for reading
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Related Articles
- Senior level DS at FAANG - what coding interviews to expectWorked at FAANG up until a month ago as mid level DS and now I'm getting callbacks for senior level roles from similar companies. My stats intuition/case studies are pretty good since that's mostly what my last job relied on. However, my coding is so rusty since I just used AI most of the time to move fast and cleaned it up when there was a mistake. I'm mostly concerned about prepping the coding and data manipulation rounds. What level of prep should I prepare for to feel 'good enough'? Should I be expected to do leetcode mediums or is pandas/sql enough? Is describing the solution and logic with pseudocode enough for tougher problems or do I have to take it from start to end with no help? What has your experience been like for expectations at senior level FAANG interviews? submitted by /u/LeaguePrototype [link] [comments]
- How do you keep up without burnout?DS sometimes feels like there's infinite amount of things to learn. Most recent trend has been AI engineering And it's not like AI came in so you can deprioritize something else, but instead it just gets added to the heap. So you already had this massive amount of content to know from stats & product, trad. ML, deployment, ops, engineering, cloud, etc. and then you add the new thing on and the new thing. And when you read the job descriptions they literally list of all of this. I just had an interview for a random gaming company that wanted cloud, snowflake, stats, ML, ops, and AI experience in 1 person and it was for like 3-5 years of experience. And I wish that this was a one off thing but it seems to get more common. It actually feels like FAANG is easier to interview for because they silo people and not expect you to know and do everything What is your strategy for learning these skills without getting exhausted, or do you feel companies expectations are overflated? Is this a by product of AI where people are expected to do a lot more with less? submitted by /u/LeaguePrototype [link] [comments]
- Interview Experience: Big teams look for potential, smaller teams look for how fast you can instantly come add valueMy interview experience has been a massively varied at this point, but what I've noticed is the massive difference between big companies like FAANG and smaller orgs like DS in banking or random small companies At FAANG it's kind of like an IQ + knowledge test (what google calls Role related knowledge) and smaller companies do assessments for very specific types of modeling or use cases, like build a model being evaluated on a certain metric. So at FAANG I was asked questions like "why is the formula for s.d. different for pop. vs sample', or 'what happens to the bias/variance in x,y,z situation' mean while at companies that are smaller and pay less they sent me a random 30-60 minute assessment and asked me to directly clean data and code up a model with sklearn/pandas. Is this what everyone else has experienced? It does seem like at smaller or traditional companies test if you will be a good code monkey while others look for actual understanding. submitted by /u/LeaguePrototype [link] [comments]
- Should coding interviews just become vibe coding interviews at this point?I don’t really get why interviews are still so focused on obscure data structures, algorithms, or complex SQL and pandas problems. At this point, most of us are using AI in some capacity to write or assist with code anyway. Why does it still matter if I can invert a binary tree in 10 minutes from memory? Wouldn’t it make more sense to talk about actual experience, ML concepts, or even do a coding exercise where AI is allowed, like how people actually work on the job? Why do you think companies are still stuck using these older methods to evaluate candidates? submitted by /u/Lamp_Shade_Head [link] [comments]
Tagged with
#natural language processing for spreadsheets#big data management in spreadsheets#generative AI for data analysis#Excel alternatives for data analysis#cognitive automation#rows.com#enterprise data management#big data performance