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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.
Fractal’s suite of businesses includes Asper.ai (enabling interconnected decisions for revenue growth) and Analytics Vidhya (one of the world’s largest data science communities). Fractal incubated Qure.ai, a global healthcare AI leader enhancing the rapid identification and management of tuberculosis, lung cancer, and stroke. Fractal’s 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 MarshallGoldsmith.ai, Vaidya.ai, Kalaido.ai, and the open-source reasoning model Fathom-R1-14B.
Fractal employs over 5,000 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 India’s Best Companies to Work For (Top 100, 2025), a ‘Great Workplace’ for eight consecutive years, and among ‘India’s 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 Group’s 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 We’re 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-learning–driven).
- 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
- Bachelor’s Degree in Statistics, Economics, Applied Math, Data Science, or related field. Master’s 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, you'll enjoy your career with us!
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Outside India: [email protected]
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