Associate Data Scientist - Marketing Mix Modeling

Associate Data Scientist

, India

Job Family Group:

Research and Development

Worker Type:

Regular

Posting Start Date:

February 3, 2026

Business Unit:

Finance

Experience Level:

Early Careers

Job Description:

Associate Data Scientist (MMM)

What's the role

• 3+ years of relevant MMM experience (Practical 2+ years’ experience in long-term marketing mix modelling)
• Build long-term Marketing Mix Models (MMM) using advanced tools tailored to specific business challenges.
• Identifies the right set of models suitable for long-term MMM modeling and develops the right code / package to execute them
• Select appropriate modeling techniques and develop custom code/packages to implement them effectively.
• Lead data preparation, exploratory analysis, and iterative modeling processes specific to long-term MMM.
• Deliver actionable insights on how brand media impacts long-term brand equity, sales, and margins.
• Evaluate the scientific rigor and business relevance of complex long-term Marketing Mix Models (MMM). Possess a deep understanding of existing MMM frameworks and demonstrate the ability to leverage short-term MMM models into building long-term MMM models.
• Engage with Shell stakeholders and Line managers to ensure timely and quality project delivery.
• Strong proficiency in Python, SQL, Git, Databricks, and understanding of object-oriented programming principles.

What we need from you

• Good knowledge of Marketing domain, ATL/BTL marketing and clear understanding of concepts like adstock/carryover, saturation etc.
• Proven experience in building MMM models to capture the long-term impact of Marketing on Sales and Brand Equity is a must.
• Strong programming skills in Python and SQL.
• Good to have - Understanding of data engineering concepts, including data pipelines, ETL processes, object-oriented programming and general software engineering principles to build scalable and reusable analytical products.
• Understands life cycle of a generic data science project (from problem statement to model deployment)
• Exposure to causal inference is a plus.
• Hands on experience with at least 2 MMM techniques below:
• Mixed-effects models (random and fixed effects)
• Hierarchical linear models
• Bayesian modelling (e.g., Bayesian MMM)
• Structural equation modeling (SEM)
• Regularized Regression techniques
• Ability to explain complex ML models and analytical concepts in simple terms to business stakeholders.
• Good storytelling and presentation skills to effectively communicate insights and influence decisions.
• Collaborate effectively with stakeholders, including line managers and cross-functional teams, to accelerate project delivery and ensure alignment with expectations.

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Experience Level

Mid Level