Key Accountabilities

• Lead development of high-impact predictive and prescriptive models across multiple domains (advanced segmentation, personalization, pricing, forecasting, advanced measurement)
• Design and oversee complex experiments, including A/B and multivariate and tests
• Drive uplift modeling and long-term value prediction to optimize targeting and offers
• Research/adapt methods for hard problems; lead PoCs; scale viable solutions
• Partner with business leaders to identify high-value opportunities for predictive model deployment
• Co-design MLOps (feature store, experimentation, monitoring) with DE/P&T; ensure interoperability & governance
• Apply causal inference techniques for marketing and product measurement.
• Publish technical best practices and reusable frameworks
Education

Bachelor’s or Master’s in a quantitative field (ex: mathematics, statistics, data science) or an equivalent combination of education and work related experience

Experience

5-8 years of relevant work experience in Data Science with business-facing impact delivery

Technical Skills and Knowledge

• Strong SQL (advanced queries, stored procs, performance tuning) and Python programming skills
• Advanced SQL, Python, and ML and LLM frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch, Gemini)
• Proficiency deploying models in cloud environments (GCP - BigQueryML & Vertex AI, AWS SageMaker)
• Proficiency in causal inference, optimization algorithms, and advanced experiment design
• Expert in GCP/Vertex AI and MLOps orchestration tools
• Advanced model governance and performance monitoring skills
• Strong grasp of business impact modeling (ROI, LTV, incrementality)
• Independent problem solver with strong critical thinking skills and business acumen
• Strong statistical modeling (logistic/linear regression, time series, propensity modeling)
• Experimental design (A/B, multivariate)
• Feature engineering best practices