Tell me about yourself: answer for data scientist
Data science interviewers separate people who ship models from people who only train them in notebooks. Say where your model ran, what it changed and how you checked that it worked.
Write your ownA sample answer for a data scientist
The name, place and numbers in this sample are made up. Use it to see the shape of a good answer, then tell your own story.
Sample from Tanvi, a data scientist with 3 years at a lending startup in Bengaluru, in a job switch interview
About 1 min 5 sec 149 words
Presentwho I am now
Hello, I'm Tanvi Bhat, a data scientist with three years at a lending startup in Bengaluru. I build credit-risk models in Python, mostly with XGBoost and scikit-learn, and I look after them after they go live.
Pastwhat I have done
Last year, I rebuilt our loan-default model with better features from repayment history. At the same approval rate, it caught about thirty percent more risky applicants than the old model. I tested it for two months alongside the old one before switching, and I set up a monthly check for drift. The credit team now uses its score in every decision.
Futurewhy this role
I'd like to work on a problem with richer data and more experiments. Your fraud team has both, and I want to learn how large-scale models are monitored in production. I can join after a sixty-day notice period, and I'm happy to walk your team through my model case study in detail.
Short version
Three sentences, for when you're nervous.
I'm Tanvi, a data scientist with three years building credit-risk models in Python at a lending startup. My rebuilt default model caught about thirty percent more risky applicants at the same approval rate. I'd like to join a team working on fraud and large-scale monitoring.
3 things to include
A model that went live
Say where it runs and who uses its output. Deployed models stand out from notebook projects.
How you checked it
A side-by-side test or a drift check tells the panel you are careful with results.
The business effect
More risky applicants caught at the same approval rate is a result a finance head can read.
2 things to avoid
Algorithm lists
Naming ten methods is less convincing than explaining why you picked XGBoost for this problem.
Accuracy alone
A single accuracy number hides imbalanced data. Use the metric that fits the problem.
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