Data Scientist — Mid-Level Example

@ Nexus Commerce — Boston, MA

• >>Built a gradient-boosted churn prediction model (XGBoost) with 89% AUC-ROC, enabling the customer success team to proactively retain at-risk accounts and saving $1.8M in annual revenue
• >>Developed a collaborative filtering recommendation engine that increased average order value by 12% and click-through rate by 8% across 500K monthly users
• >>Designed and deployed A/B testing infrastructure using Bayesian analysis, enabling product teams to run 40+ experiments per quarter with statistically rigorous results
• >>Productionized 6 ML models using AWS SageMaker and MLflow, implementing automated retraining pipelines and model monitoring dashboards

@ Beacon Analytics — Cambridge, MA

• >>Performed exploratory data analysis on 10M+ row datasets using Python (pandas, matplotlib, seaborn) to identify revenue growth opportunities for 5 enterprise clients
• >>Built NLP text classification models (BERT fine-tuning) to automate customer ticket routing, achieving 92% accuracy and reducing manual triage by 60%
• >>Created interactive dashboards in Looker for C-suite stakeholders, translating complex model outputs into actionable business recommendations