Lead Scientist - Marketing Science

Location: Bengaluru, India

Department: Marketing Measurement

Experience: 8–12 years

Skills: Marketing Mix Modeling (MMM), Statistical Modeling, Incrementality, Time Series Analysis, Causal inference techniques, Econometrics, Forecasting

Role Overview

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

2. 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

3. 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

7. 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