Principal Data Scientist

• Define and evolve analytical methodologies, standards, and best practices across the Data Science team and broader Enterprise Data & AI organization
• Shape the analytical roadmap and prioritize investments based on business value, feasibility, decision risk, and data readiness
• Serve as the senior technical escalation point for complex statistical, experimental, modeling, and measurement questions
• Provide technical direction for major cross-functional, enterprise-wide initiatives
• Lead novel, high-complexity analyses supporting strategic and high-consequence decisions
• Establish standards for hypothesis testing, power analysis, confidence intervals, effect-size reporting, multiple-comparison control, sensitivity analysis, and uncertainty quantification
• Define causal inference approaches and enterprise experimentation frameworks
• Lead advanced time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization
• Define standards for predictive and machine learning model design, validation, explainability, monitoring, and lifecycle management
• Provide technical oversight for critical predictive and machine learning models and guide their productionization
• Partner with senior leaders to frame ambiguous strategic challenges into analytical programs with explicit decisions, hypotheses, success measures, and value expectations
• Advise leaders on measurement strategy, experimentation, uncertainty, risk, and trade-offs
• Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness
• Lead independent technical reviews of high-impact analyses and models
• Create reusable frameworks, libraries, templates, and reference implementations
• Mentor Data Scientists and Senior Data Scientists and raise technical standards through coaching, reviews, and learning sessions
• Influence hiring standards, interview practices, and technical assessment criteria
• Partner with Insights & Analytics, Analytics Engineering, Data Engineering, AI Engineering, MLOps, governance teams, domain leaders, and product owners
• Communicate methods, findings, limitations, and recommendations to executives, business stakeholders, technical peers, and governance audiences
• Lead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities advancing responsible data science

• 10+ years of relevant professional experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline
• Demonstrated track record of providing technical leadership for complex, cross-functional analytical initiatives with material business impact
• Expert-level knowledge of statistical methodology, including experimental design, causal inference, regression, forecasting, Bayesian methods, and machine learning
• Deep proficiency in Python and modern data science libraries, including pandas, Num Py, Sci Py, stats models, and scikit-learn
• Extensive experience developing and validating predictive models and supporting their transition into production environments
• Demonstrated ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions
• Proven ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication
• Strong experience conducting technical reviews, establishing analytical standards, and mentoring experienced practitioners
• Hands-on experience with Databricks or a comparable cloud-based data and analytics platform
• Exceptional written and verbal communication skills, including the ability to explain complex quantitative concepts to non-technical audiences
• Master's degree or PhD in statistics, mathematics, econometrics, operations research, computer science, engineering, or a related quantitative field (preferred)
• Experience in a regulated industry such as medical devices, healthcare, pharmaceuticals, life sciences, or financial services (preferred)
• Demonstrated depth in one or more areas such as experimentation, causal inference, forecasting, survival analysis, optimization, or advanced machine learning (preferred)
• Experience establishing analytical standards, governance practices, reusable frameworks, or enterprise data science capabilities (preferred)
• Publications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership (preferred)
• Experience advising executive leaders on strategic decisions through quantitative evidence (preferred)
• Experience working across geographically distributed, multidisciplinary teams (preferred)

Expertise in statistical methodology, machine learning, and data science, with a strong focus on experimental design, causal inference, and predictive modeling. Proven ability to lead complex analytical initiatives and communicate findings effectively to diverse…