Data science resumes suffer from a credibility problem: thousands of them list the same Titanic and MNIST projects. The ones that get interviews show messy data, a deployed model, and a business metric that moved.
What separates a real project from a tutorial
- Data you had to obtain or clean yourself — not a ready CSV from Kaggle
- A baseline you beat — 'improved on the existing rules-based system by 14 points of recall'
- A deployment — an API, a batch job, a dashboard someone uses
- A monitoring story — what you did when the model drifted
Write the model bullet properly
Built a machine learning model using Random Forest for customer churn prediction with 92% accuracy.
Built a churn model (gradient boosting, 180k customers, 42 features) that raised retention-campaign precision from 0.21 to 0.38; deployed as a weekly batch scoring job feeding the CRM.
Accuracy on an imbalanced problem is the giveaway that a project was never evaluated seriously. Name the metric that suits the problem.
Metrics: pick the honest one
Precision, recall, F1, AUC, RMSE, MAPE — choosing the right one and reporting it against a baseline signals more maturity than any accuracy number. Reporting a single accuracy figure on a skewed dataset signals the opposite.
LLM and GenAI work, described credibly
Half of 2026's applicants list 'LLM' and 'RAG'. What distinguishes a real implementation is the operational detail: chunking strategy, retrieval evaluation, latency and cost per query, hallucination handling, and how you measured quality.
'Built a RAG chatbot using LangChain and OpenAI' is the resume equivalent of a tutorial. 'Cut support-ticket deflection cost 31% with a RAG assistant over 9k help articles; hybrid BM25+embedding retrieval, evaluated on 300 labelled questions (recall@5 0.87), ₹0.4 per query' is a job.
Structure for an ML resume
- Summary with your strongest domain and deployment experience
- Experience, with model bullets written as above
- Projects — two, both meeting the 'real project' bar
- Skills — languages, ML libraries, MLOps, cloud, data tools
- Publications or competition rankings, if genuinely notable
Put this into practice
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