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Data Scientist - MMM

Actively Reviewing

Prescience Decision Solutions (A Movate Company)

Bengaluru Full-Time 4–8 yrs exp Posted 1 day ago · Apply by Sep 14, 2026

Job Description

4+ 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.

Requirements

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 the 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 communicate insights effectively and influence decisions.

Collaborate effectively with stakeholders, including line managers and cross-functional teams, to accelerate project delivery and ensure alignment with expectations.

Benefits