Statistics MS graduate with published Kaggle work and two deployed portfolio models in Python and scikit-learn. Built a demand-forecasting model that beat the baseline 18% on RMSE and shipped it as a Flask API with drift monitoring. Seeking an entry-level data scientist role in forecasting or experimentation.
Early-career data scientist from a computational biology background, fluent in Python, SQL, and PyTorch. Trained an NLP classifier reaching 0.91 F1 on 50K support tickets and containerized it with Docker for batch scoring. Eager to grow in production ML and causal experimentation.
Data Scientist with 4 years building predictive models that drove $2.3M in incremental annual revenue. Shipped a churn model (AUC 0.88) and a pricing model to production on AWS SageMaker, and ran the A/B tests that validated them. Strong across Python, SQL, and the full model lifecycle.
Applied scientist with 5 years in recommendation and ranking systems. Deployed a transformer-based recommender that lifted engagement 31% and $2.2M in revenue, serving 500K daily users behind a low-latency API. Comfortable owning a model from experiment design through MLflow deployment and monitoring.
Senior Data Scientist with 8 years turning ambiguous business problems into deployed ML. Built an experimentation platform enabling 35+ concurrent tests, tripled experiment throughput with CUPED, and mentored 5 scientists. Deep expertise in causal inference, forecasting, and MLOps on GCP.
Senior data scientist and ML lead specializing in production reliability. Cut model inference cost 40% ($30K/month) by distilling and quantizing deep models, and reduced retraining time from 12 hours to 90 minutes with an Airflow pipeline. Sets the team's experimentation and model-review standards.
Career-changer from quantitative finance, now a data scientist after a graduate program and three deployed models. Combines strong statistics and Python with new fluency in deep learning and MLOps. Brings rigor in experimentation and model validation to product and growth teams.
Writing a summary that lands
Do
- Prove the model shipped: name the deployment path (SageMaker, Docker, an API) and the monitoring, because production ML is what separates strong candidates.
- Pair every model with a business metric — revenue, churn, cost, engagement — not just AUC or F1; hiring managers read impact first, accuracy second.
- List Python and its libraries as flat, comma-separated text (Python, NumPy, pandas, scikit-learn); nested forms like "Python (pandas)" can hide library names from ATS parsers.
- Show experimentation, not just modeling: A/B tests, causal inference, and readouts signal you can measure impact, which most postings now require.
- Mirror the job description's stack and cloud (AWS, GCP, or Azure) exactly — ATS keyword matching is literal, so write "scikit-learn" and "A/B testing" as the posting does.
Avoid
- Listing models and libraries with no bullet showing a deployed model or a business outcome.
- Reporting only offline metrics (AUC, F1) with no revenue, cost, or engagement impact attached.
- Claiming "machine learning" broadly without naming the algorithms, framework, or problem type.
- Ignoring deployment and MLOps — a notebook-only resume reads as junior regardless of model quality.
- Nesting skills as "Python (pandas, NumPy)", which some ATS parsers fail to index fully.