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Hi Fishes, Sometime back I was interviewed for Technical Support Job role in my domain. T1 went well. T2, in my understanding was better only (not great like T1 but not blunder. I felt it was nice and i replied majority of questions). They released the feedback after 10 days with "Not Positive". I am not totally sure with feedback as I replied majority of questions correct. I am being bit curious with "Microsoft" tag. What can be the reason?
Discussion appreciated.
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Ok. You have a toolchest of how, but lack the fundamentals in the why and likely the which.
Companies don't invest in data science for the lols. They do it, because they expect the investment will pay them more than they invest in - and more importantly, the rate of return is demonstrably better than a different option.
Additionally, there is some data which is easier to acquire or get a forecast for, and other which is less malleable. Some data you can use for qualification and classification, and other data which is quantifiable. What I want you to think about is: what can I do with the data provided to learn something new that will make money.
So given a few types of data, what kinda of questions can you answer, how would that information or insight be used in business, and can I quantify a financial impact.
Some of that stuff comes with experience, but some of it is an opportunity to talk through how you'd figure out and prioritize things. When in doubt ask questions and structure your answer for how you'd solve it. State assumptions. State what you don't know. Ask if you can leverage perplexity to rank / benchmark
Just... recognize that I don't care about whether you can use pytorch to bag my data. Instead, I want to know whether you are going to look for ways to reuse your analysis or what additional element you might need to gain influence or intelligence for the company.
Hi, thank you for articulating this so clearly, what you’re describing is a very common challenge, even among strong data scientists. The gap you’re feeling usually isn’t about technical ability, but about structuring your thinking in a way that connects models and outputs to business decisions.
One helpful approach is to practice framing your answers around problem → approach → insight → impact. Start with the business problem, explain why your technical choice made sense, and end with what decision or outcome it enabled. Interviewers are often listening for clarity and reasoning rather than perfect technical depth.
I also know someone who helped my brother strengthen this exact skill by working on structured storytelling, interview frameworks, and confidence when discussing business impact. If you’d like guidance, you can reach out at
alfredpetersonhiringteam.gmail.com.
You’re clearly aligned with your path, once you build a repeatable structure for explaining your work, your confidence will follow and interviews will feel much more natural.