Data Scientist Resume

For most candidates, one page is ideal up to ~5 years of experience, and two pages is acceptable for senior roles with multiple shipped models. Prioritize quantified impact, production details, and the most relevant projects. Remove older tools, unrelated coursework, and duplicate bullets to keep scan time low.

Common ATS filters include Python, SQL, machine learning, statistics, A/B testing, feature engineering, model deployment, and MLOps. Add stack-specific keywords that match the job posting (e.g., MLflow, Spark, SageMaker, Databricks). Only include keywords you can defend in interviews with examples and metrics.

Yes, if the work is polished and easy to evaluate. Link to 1–2 projects with a clear README, reproducible environment, and a short summary of results (e.g., AUC 0.83, MAPE 12%). Avoid dumping many links. One strong project that mirrors the target role beats five unfinished notebooks.

Use proxy metrics and operational outcomes: reduced manual effort (hours/week), improved forecast accuracy (MAPE), lowered incident rate, reduced latency, or improved decision quality. If revenue impact is confidential, describe directional effects and adoption, e.g., “used by 35 analysts weekly” or “enabled 14 experiments/quarter.”

In most English-speaking markets, a photo is not expected and may be discouraged, especially in the US and many UK processes. Focus instead on a strong headline, a results-based summary, and clean ATS formatting. If a role explicitly requests a photo due to local norms, follow the employer guidance.

Skills should be a short, scannable list of tools and methods you actually used (Python, SQL, Spark, MLflow, A/B testing). Experience should prove those skills with context and numbers: what you built, at what scale, how you measured performance, and what changed for the business or platform.