Senior Data Scientist (Market Mix Modelling)

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Job Description

Senior Data Scientist (Market Mix Modelling)

Location: Bengaluru

About Fractal: Fractal is a globally recognized Enterprise AI company with a vision to power human decision in the enterprise.

Fractals suite of businesses includes (enabling interconnected decisions for revenue growth) and Analytics Vidhya (one of the worlds largest data science communities). Fractal incubated a global healthcare AI leader enhancing the rapid identification and management of tuberculosis lung cancer and stroke. Fractals dedicated AI research team is focused on foundational AI advancements including knowledge-based foundational models reasoning-based systems and agentic systems. The team has launched successful products such as and the open-source reasoning model Fathom-R1-14B.

Fractal employs over 5000 professionals across global locations including the United States Canada UK Netherlands Ukraine India Singapore South Africa UAE and Australia. It has consistently earned recognition as one of Indias Best Companies to Work For (Top) a Great Workplace for eight consecutive years and among Indias Best Workplaces for Women for five years running by the Great Place to Work Institute. Fractal was also named a Leader in the 2025 Forrester Wave for Customer Analytics Service Providers and earned leadership positions in the Everest Group Peak Matrix Assessment 2025 for AI and Analytics Services and Information Services Groups 2024 assessments for Data Engineering and Data Science Services.

Key Responsibilities:

Modeling & Analytics Delivery

• Build calibrate and validate MMM models using historical media and business data
• Quantify channel impact measure ROI and derive elasticity estimates.
  Develop response curves saturation effects and optimal spend recommendations.
• Integrate experimentation outcomes (lift studies geo tests) to improve model robustness

Data Management & Pipeline Development

• Design end-to-end MMM data pipelines - including ingestion transformation and QC.
• Manage large multi-source datasets (media pricing distribution promotions
  seasonality).
• Automate recurring MMM runs and ensure reproducibility of analytics workflows.

Insights & Stakeholder Engagement

• Translate complex model outputs into clear business-friendly insights.
• Drive conversations with marketing finance and media teams on channel shifts.
• Present findings to senior stakeholders (CMO growth leaders digital teams).
• Recommend budget allocations and simulate what-if scenarios.

Operationalization & Continuous Improvement

• Build optimization engines for budget planning
• Monitor model performance and recalibrate based on new data
• Ensure governance documentation and version control of MMM models
• Collaborate with data engineers product teams media agencies and business analysts

What Were Looking For

• 5 to 8 years of experience in marketing analytics MMM media effectiveness or econometrics
• Prior experience in media agencies consulting firms or digital marketing analytics is a plus
• Hands-on experience with MMM frameworks (Bayesian frequentist machine-learningdriven).
• Advanced proficiency in Python or R for econometric modelling
• Experience with cloud data environments (AWS/GCP/Azure)
• Knowledge of optimization techniques (linear programming gradient-based optimizers)
• Strong understanding of digital marketing KPIs across channels
• Experience working with Nielsen Kantar Facebook Lift or Google Geo-Experiments data
• Exposure to MTA incrementality testing and customer journey analytics

Technical & Modeling Skills

• Strong econometric modeling: linear/non-linear regression Bayesian MMM hierarchical models
• Expertise in ad-stock diminishing returns saturation curves elasticity estimation
• Solid understanding of causal inference (geo experiments causal impact synthetic controls)
• Experience with time-series modeling and lag structures
• Proficiency in machine learning: regularized regression gradient boosting hybrid MMM
• Strong statistical foundations: hypothesis testing multicollinearity variable selection

Data & Engineering Skills

• Advanced SQL Python/R (stats models PyMC Stan scikit-learn)
• Experience in data cleaning transformation feature engineering for media/marketing datasets
• Handling multi-granular datasets (daily/weekly campaign-level spend impressions)
• Familiarity with cloud platforms (GCP/AWS/Azure) and big-data tools (Spark Databricks BigQuery)
• Building automated model pipelines and reproducible codebases

Marketing & Business Skills

• Understanding of media channels (TV Digital Search Social OOH Retail)
• Ability to compute ROI ROAS marginal ROI and contribution splits
• Knowledge of attribution frameworks: MMM vs MTA vs experimentation
• Strong storytelling: turning model outputs into actionable business recommendations
• Budget optimization & scenario planning expertise

Desired Qualification

• Bachelors Degree in Statistics Economics Applied Math Data Science or related field. Masters Degree preferred.
• Strong communication and storytelling for C-level presentations
• Ability to work in fast-paced cross-functional environments
• High problem-solving orientation structured thinking and business-first mindset

If you like wild growth and working with happy enthusiastic over-achievers youll enjoy your career with us!

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