Lead Scientist - Marketing Science

Role Overview

The Senior Manager – Marketing Science will lead the development of advanced econometric and statistical models to solve complex marketing measurement problems. The role is anchored in scientific rigor, requiring strong foundations in econometrics, causal inference, and statistical learning. This position focuses on building robust, interpretable, and scalable models—particularly in Marketing Mix Modeling (MMM)—while ensuring causal validity and statistical soundness. The role also involves guiding a team of data scientists and contributing to the evolution of measurement methodologies within the organization. Core Scientific Focus Formulate business problems as quantitative and statistical models Develop econometric frameworks that balance interpretability and predictive power Ensure causal validity and minimize bias in observational data Apply rigorous model validation, diagnostics, and hypothesis testing Advance the use of Bayesian, hierarchical, and time series models in marketing science Key Responsibilities 1. Econometric Modeling & MMM Development Lead the design and implementation of Marketing Mix Models (MMM) Build models using: Regression-based approaches (linear and non-linear) Time series modeling for trend, seasonality, and forecasting Bayesian and hierarchical modeling approaches

Incorporate key modeling constructs: Adstock and lag effects Saturation and diminishing returns External drivers and control variables

Ensure statistical robustness through: Proper model specification Parameter stability and interpretability Sensitivity analysis

1. Causal Inference & Measurement Design Apply causal inference methods such as: Difference-in-differences Synthetic control Experimental and quasi-experimental designs

Design and evaluate incrementality frameworks, including: Geo experiments and holdout testing

Address: Endogeneity and confounding Bias in observational datasets

1. Statistical Validation & Diagnostics Perform rigorous model validation including: Residual analysis and diagnostics Multicollinearity checks Out-of-sample validation and cross-validation

Evaluate model performance using appropriate statistical metrics and ensure alignment with business outcomes 4. Analytical Frameworks & Model Integration Develop integrated frameworks combining: MMM, attribution, and experimentation

Apply machine learning techniques where they enhance model performance without compromising interpretability Build scalable and reusable modeling approaches 5. Scientific Communication Translate complex statistical outputs into structured and interpretable insights Clearly articulate model assumptions, limitations, and implications Support decision-making through evidence-based recommendations 6. Team Leadership & Scientific Capability Building Lead and mentor a team of data scientists Establish best practices in: Statistical modeling Code quality and reproducibility

Build capability in advanced topics such as: Bayesian modeling Causal inference Experimental design

1. Research & Continuous Improvement Stay updated with advancements in: Econometrics and marketing science Causal inference and statistical learning

Evaluate and implement improved methodologies to enhance modeling accuracy and reliability 8. Delivery & Collaboration Manage multiple analytical projects with defined timelines and quality standards Collaborate with cross-functional teams to ensure alignment between modeling outputs and business needs Required Skills & Experience 8–12 years of experience in advanced analytics, econometrics, or marketing science Strong hands-on experience in Marketing Mix Modeling (MMM) Technical Expertise Econometrics and regression modeling Time series analysis and forecasting Bayesian statistics and hierarchical modeling (preferred) Causal inference and experimental design Programming & Tools Proficiency in Python or R Strong experience with SQL and large datasets Preferred Qualifications Master’s or PhD in: Statistics / Econometrics / Mathematics / Operations Research / Engineering

Experience in marketing analytics or advertising measurement Exposure to SaaS-based analytics platforms Key Competencies Strong quantitative and analytical reasoning Scientific rigor and attention to detail Structured problem-solving ability Clear and precise communication of technical concepts Ability to mentor and develop scientific talent What Success Looks Like Development of statistically sound and interpretable MMM models Demonstrated causal impact on marketing decision-making Scalable and repeatable modeling frameworks Strong internal capability in marketing science