Senior Statistical Modeler

Senior Statistical Modeler / Market Intelligence Data Scientist Location Contract Type Contract (~6 months) About the Role We are seeking an experienced Statistical Modeler to support the design and implementation of a large-scale market intelligence and analytics platform. The role will focus on developing robust, transparent, and defensible estimation methodologies that combine multiple data sources to generate market size, market share, competitive intelligence, and business performance insights. This is a highly applied role requiring a blend of statistical expertise, commercial analytics experience, and practical delivery skills. The ideal candidate will be comfortable working with imperfect real-world datasets, developing estimation frameworks, quantifying uncertainty, and communicating methodology to both technical and business stakeholders. The successful candidate will work alongside data engineering, analytics, reporting, and business teams to build a scalable market intelligence capability supporting executive decision-making. Key Responsibilities Market Estimation & Statistical Modeling • Design and implement market sizing and market share estimation methodologies. • Develop coverage-adjusted projection models using partial or sampled data sources. • Create weighting and expansion factor methodologies to project from observed data to total market estimates. • Design stratified estimation approaches across channel, geography, retailer, product, and category dimensions. • Develop confidence interval methodologies and uncertainty quantification frameworks. • Build statistically defensible approaches for combining multiple independent data sources. Data Integration & Triangulation • Design methodologies for multi-source triangulation across internal and external datasets. • Develop approaches for reconciling conflicting signals from different data providers. • Establish statistical frameworks for data quality assessment and source reliability scoring. • Support integration of panel data, scanner data, ecommerce data, syndicated data, and operational business data. Validation & Model Governance • Design backtesting frameworks to evaluate model performance. • Define model accuracy metrics and monitoring approaches. • Conduct sensitivity testing and scenario analysis. • Create model stability assessments and drift monitoring processes. • Document all assumptions, methodologies, calculations, and validation approaches. Advanced Analytics • Develop forecasting and predictive models where appropriate. • Apply statistical and machine learning techniques to improve estimation accuracy. • Evaluate advanced methodologies including Bayesian modeling, hierarchical models, ensemble approaches, and time-series forecasting. • Recommend enhancements to improve confidence and reliability over time. Stakeholder Engagement • Explain methodology and results to non-technical stakeholders. • Participate in workshops and methodology reviews. • Present findings, assumptions, and limitations clearly. • Support executive-level discussions regarding confidence, reliability, and interpretation of results. Required Experience Statistical Modeling • 7+ years of hands-on statistical modeling experience. • Strong background in applied statistics, econometrics, biostatistics, data science, quantitative analytics, or a related discipline. • Demonstrated experience building estimation models from incomplete or sampled datasets. • Experience calculating and interpreting confidence intervals, variance estimates, and uncertainty measures. Commercial Analytics Experience Experience in one or more of the following domains: • Retail analytics • Consumer packaged goods (CPG) • Market measurement • Market research • Healthcare analytics • Pharmaceutical analytics • Consumer insights • Ecommerce analytics • Commercial forecasting Statistical Techniques Strong experience with several of the following: • Sampling methodologies • Weighting methodologies • Expansion factor modeling • Ratio estimation • Stratified estimation • Hierarchical modeling • Bayesian statistics • Confidence interval estimation • Forecasting and time-series analysis • Regression modeling • Multivariate analysis • Causal inference • Sensitivity analysis • Simulation techniques • Monte Carlo methods Technical Skills Required: • Python • SQL • Statistical modeling libraries • Data analysis and visualization tools Preferred: • R • Snowflake • Databricks • Power BI • Tableau