Senior manager statistical programming Jobs

Lead Scientist - Marketing Science
Lifesight —India
- Full-time
- Doctoral degree
- Master's degree
- SQL

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Lead Scientist - Marketing Science

Lifesight -
India

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Job details

Full-time

Qualifications

Statistics
Econometrics
Statistics
Doctoral degree
R
Marketing mix modeling
Master's degree
SQL
Machine learning
Forecasting
Leadership
Python
Marketing
Analytics

Full job description

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

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

Strong internal capability in marketing science

Apply Now

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