Lead Marketing Scientist

Lifesight; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Lifesight is seeking a Lead Marketing Scientist to head a team of marketing scientists in developing advanced econometric and statistical models for marketing measurement. The role focuses on Marketing Mix Modeling (MMM) with strong emphasis on causal validity and interpretability.

You will mentor a team of data scientists, shape measurement methodologies, and deliver statistically sound insights across campaigns. This is a senior, hands-on leadership role in Bengaluru, India.

Qualifications

• 8–12 years in advanced analytics, econometrics, or marketing science, with strong hands-on MMM experience.
• Strong hands-on experience in Marketing Mix Modeling (MMM) and econometrics.
• Proficiency in Python or R, with SQL and large datasets.

Responsibilities

• Lead the design and implementation of MMMs, formulating business problems as rigorous quantitative models.
• Apply causal inference methods and design endogenous tests to validate increments.
• Perform rigorous model validation, diagnostics, and cross‑validation.
• Develop integrated measurement frameworks combining MMM, attribution, and experimentation.
• Translate complex statistical outputs into actionable insights for technical and non-technical audiences.
• Lead and mentor a team of data scientists, establishing best practices in modeling and reproducibility.
• Stay current with econometrics and marketing science advancements and drive methodology improvements.

Skills

MMM

Econometrics

Regression modeling

Time series

Causal inference

Bayesian statistics

Python

R

SQL

Education

Master's or PhD in Statistics/Econometrics/Mathematics/OR Engineering

Tools

Python

R

SQL

Job description

About the Role

Lifesight is seeking a Lead Marketing Scientist to head a team of marketing scientists in developing advanced econometric and statistical models that solve complex marketing measurement problems. The role is anchored in scientific rigor and demands strong foundations in econometrics, causal inference, and statistical learning.

You will focus on building robust, interpretable, and scalable models - particularly in Marketing Mix Modeling (MMM) - while ensuring causal validity and statistical soundness. Beyond hands‑on modeling, you will mentor a team of data scientists and shape the evolution of measurement methodologies across the organization.

Key Responsibilities

Econometric Modeling MMM Development

• Lead the design and implementation of Marketing Mix Models (MMM), formulating business problems as rigorous quantitative and statistical models.
• Build models using regression-based approaches (linear and non‑linear), time series methods for trend, seasonality, and forecasting, and Bayesian and hierarchical modeling frameworks.
• Incorporate core marketing science constructs, including adstock and lag effects, saturation and diminishing returns, and external drivers and control variables.
• Ensure statistical robustness through proper model specification, parameter stability and interpretability, and sensitivity analysis.

Causal Inference Measurement Design

• Apply causal inference methods such as difference-in-differences, synthetic control, and experimental and quasi‑experimental designs.
• Design and evaluate incrementality frameworks, including geo experiments and holdout testing.
• Address endogeneity, confounding, and bias in observational datasets to ensure causal validity of model outputs.

Statistical Validation Diagnostics

• Perform rigorous model validation, including residual analysis and diagnostics, multicollinearity checks, and out‑of‑sample and cross‑validation.
• Evaluate model performance using appropriate statistical metrics and ensure alignment with business outcomes.

Analytical Frameworks Model Integration

• Develop integrated measurement frameworks that combine MMM, attribution, and experimentation.
• Apply machine learning techniques where they enhance model performance without compromising interpretability.
• Build scalable, reusable modeling approaches that strengthen the organization s measurement methodology.

Scientific Communication

• Translate complex statistical outputs into structured, interpretable insights for technical and non‑technical audiences.
• Clearly articulate model assumptions, limitations, and implications, and support decision‑making with evidence‑based recommendations.

Team Leadership Capability Building

• Lead and mentor a team of data scientists, establishing best practices in statistical modeling, code quality, and reproducibility.
• Build team capability in advanced topics such as Bayesian modeling, causal inference, and experimental design.

Research, Delivery Collaboration

• Stay current with advancements in econometrics, marketing science, causal inference, and statistical learning, and implement improved methodologies that enhance modeling accuracy and reliability.
• Manage multiple analytical projects to defined timelines and quality standards.
• Collaborate with cross‑functional teams to ensure modeling outputs align with business needs.

Required Skills Experience

• Experience: 8-12 years in advanced analytics, econometrics, or marketing science, with strong hands‑on experience in Marketing Mix Modeling (MMM).
• Technical expertise: Econometrics and regression modeling; time series analysis and forecasting; causal inference and experimental design; Bayesian statistics and hierarchical modeling (preferred).
• Programming tools: Proficiency in Python or R, with strong experience in SQL and working with large datasets.

Preferred Qualifications

• Master s or PhD in Statistics, Econometrics, Mathematics, Operations Research, Engineering, or a related quantitative discipline.
• Experience in marketing analytics or advertising measurement.
• Exposure to SaaS‑based analytics platforms.

Key Competencies

• Strong quantitative and analytical reasoning, with scientific rigor and attention to detail.
• Structured problem‑solving and the ability to formulate ambiguous business questions as tractable statistical problems.
• Clear, precise communication of technical concepts to diverse audiences.
• Ability to mentor and develop scientific talent.

What Success Looks Like

• You create/help create statistically sound, interpretable MMM models delivered within Lifesight s methodology framework.
• You Improve modeling efficiency by leveraging best‑in‑class AI to automate tasks and reduce manual effort.
• You help create a high‑performing, well‑mentored team of marketing science professionals at lifesight.
• Our clients are consistently satisfied and trust and act on our measurement insights.

Required Skills

Causal inference techniques Marketing Mix Modeling (MMM) regression analysis Time Series Analysis Econometrics python Statistical Modeling Marketing Analytics bayesian

Disclaimer: This job posting & Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.