Data science and AI resumes in Bengaluru: production versus notebook
Bengaluru has more data science applicants per opening than any other Indian city, and hiring managers have converged on one screening question: did anything you built reach production and stay there.
A resume of notebooks, Kaggle placements and course projects answers no. That is not a judgement on ability — it is a statement about what the role involves, which is latency budgets, drift, retraining and a business owner who wanted a different answer.
Mark production explicitly
Which models went live, how many users or decisions they touched, how long they stayed in production. This single distinction separates most Bengaluru shortlists, and most resumes leave it ambiguous.
If nothing reached production, say what stage it reached — pilot, evaluated prototype, internal tool — honestly. Overstating deployment fails in the first technical conversation.
The business metric, not the model metric
AUC from 0.81 to 0.87 means little to a hiring manager. Churn reduced, fraud caught, cost avoided, revenue attributed — that is the sentence they repeat to their own manager when advocating for you.
Give both where you have them: the model improvement and what it did to the business number. The second is what gets the interview.
Data scale and how dirty it was
Rows, sources, update frequency, and the state it arrived in. Clean benchmark data is not evidence of the skill being hired for. A pipeline built over six inconsistent source systems is.
MLOps is the half that gets omitted
Pipelines, feature stores, monitoring, drift detection, retraining cadence. Applied teams in Bengaluru weight this heavily and academically strong candidates consistently leave it out entirely.
LLM work described precisely
Fine-tuning, retrieval systems, evaluation and guardrails are different from calling an API. Bengaluru interviewers ask immediately and the distinction is obvious to them within one question, so vagueness costs more than a modest honest claim.
Research track and applied track need different documents
Research wants publications, novelty and method. Applied wants shipped systems, reliability and business impact. Sending one document to both is the commonest reason a strong data scientist gets no callbacks from either.
How Content Factory writes it
Content Factory interviews for what reached production and what did not, then writes the two tracks as separate documents where you are applying to both.
We describe LLM work at the level you can defend, because this is the field where inflation is caught fastest. A precise smaller claim consistently outperforms a vague larger one.
Priced as a fixed figure in writing, three to five working days, with the LinkedIn rewrite available alongside — Bengaluru data recruiters search by framework and domain rather than by title.