40+ Data Scientist Resume Bullet Points You Can Adapt Fast

Capstone: churn prediction (course)

• Built a binary classification model in Python (pandas, scikit-learn) on ~50k rows as part of a degree capstone; validated with stratified k-fold and reported precision/recall trade-offs to a non-technical audience.
• Engineered features from timestamps and usage aggregates; documented assumptions and leakage checks so results were reproducible from a clean notebook export.
• Presented model limitations and next steps in a 10-minute readout; received top marks for clarity on bias and class imbalance handling.
• Compared at least two model families with a simple hyperparameter grid; chose the best model using held-out metrics, not training accuracy alone.

Regression & statistics coursework

• Completed a semester project fitting generalized linear models in Python; interpreted coefficients and checked residual diagnostics for a public health dataset.
• Ran hypothesis tests and confidence intervals for A/B-style lab assignments; documented why p-values alone were insufficient without effect sizes.
• Built a simple power analysis spreadsheet for lab exercises; explained why underpowered tests could miss meaningful effects.
• Visualized heteroskedasticity and applied a variance-stabilizing transform; summarized limitations for a non-technical grader in two sentences.

SQL + dashboard internship

• Wrote SQL (joins, window functions) to reconcile daily revenue vs funnel events; surfaced a 6% discrepancy between marketing attribution and finance totals for the ops lead.
• Prototyped a Looker dashboard used weekly by two teams; cut ad-hoc Slack requests by replacing them with three standard views (funnel, cohort, weekly KPI).
• Documented metric definitions in a shared sheet; resolved one conflicting KPI between growth and finance before leadership review.
• Partnered with an analyst to QA a new event; fixed a tracking bug that understated sign-ups by ~4% in weekly reporting.

Kaggle-style NLP (portfolio)

• Trained a text classifier on ~20k labeled reviews; compared TF-IDF + logistic regression vs a small fine-tuned transformer baseline and reported error analysis by class.
• Packaged preprocessing and evaluation in a public repo with a one-page README so a reviewer could rerun training in under 30 minutes on CPU.
• Submitted to a class leaderboard competition; ranked top 15% by macro-F1 while keeping inference time under the stated CPU budget.
• Added a short ethics note on label noise and class imbalance; proposed next steps if the model were used beyond the coursework scope.

Exploratory analysis notebook (GitHub)

• Published an EDA notebook on a public dataset (Python, pandas, plots); summarized three actionable insights with cited visualizations for a mock stakeholder review.
• Cleaned messy categorical fields with explicit rules; logged every transform so a peer could reproduce the same tables from raw CSVs.
• Created a correlation heatmap and flagged two redundant features before modeling; reduced multicollinearity risk in a follow-on homework.
• Wrote a short “next steps” section listing modeling options and data gaps; matched rubric expectations for reproducible academic work.

Onboarding experiment roadmap

• Designed and analyzed sequential A/B tests on onboarding; recommended a flow change that lifted 7-day activation by 4.1% (95% CI 1.2-6.9%) without harming support volume.
• Partnered with engineering on event quality; improved coverage of key funnel events from 78% to 96% before scaling the experiment to 100% traffic.

Customer LTV model (batch scoring)

• Built gradient-boosted LTV estimates in Python; calibrated bins vs realized revenue over 6 months and shared uncertainty ranges with marketing for budget allocation.
• Automated monthly scoring in Airflow; reduced manual spreadsheet work by ~8 hours per month for the growth team.

NLP: ticket routing assist

• Fine-tuned a compact transformer for multi-label intent on ~40k tickets; achieved macro-F1 0.81 vs 0.68 for the legacy rules baseline in offline eval.
• Shipped shadow-mode inference behind a feature flag; monitored drift weekly and defined rollback criteria with on-call support.

Self-serve analytics hygiene

• Owned metric definitions for “active user” across mobile and web; resolved three conflicting dashboards by publishing a single source of truth doc and dbt tests.

Experimentation platform & culture

• Led adoption of a centralized experimentation service (power analysis, sequential testing guardrails); cut time-to-first-valid experiment from ~4 weeks to ~6 days for eight product squads.
• Instituted review office hours with PM and design; blocked three launches where underpowered designs would have produced misleading lift claims.

Real-time ranking / relevance

• Owned the iteration loop for a ranking model serving ~12k RPS; reduced p99 latency by 22% via batching + caching while holding offline NDCG within 0.5% of prior.
• Partnered with infra on canary deploys and automated rollback on feature-distribution drift beyond agreed thresholds.

Forecasting & planning

• Delivered probabilistic demand forecasts used in quarterly planning; reduced MAPE vs naive baseline by 18% on top SKUs and documented downside scenarios for finance.

Model risk & compliance

• Defined monitoring for fairness and stability on a credit-adjacent model; coordinated with legal and risk on documentation for regulatory review.

Mentorship & hiring

• Mentored four junior scientists on experimental design and code review standards; raised median experiment review turnaround from 6 days to 2 days.