Job Description

Key Responsibilities
Develop, implement, and optimize Marketing Mix Models (MMM) to measure the impact of marketing investments across channels and support budget allocation decisions.
Build robust Bayesian statistical models for marketing effectiveness, forecasting, uncertainty estimation, and scenario planning.
Apply causal inference methodologies to measure the incremental impact of marketing campaigns and distinguish correlation from causation.
Design and execute advanced statistical modelling techniques including regression analysis, hierarchical Bayesian models, time-series analysis, and probabilistic modelling.
Develop attribution and incrementality measurement frameworks using experimental and observational data.
Conduct hypothesis-driven experimentation, including A/B testing, geo experiments, holdout testing, and lift measurement.
Analyze large-scale marketing and media datasets to generate actionable business insights.
Build automated dashboards and reporting solutions using Power BI or Looker Studio.
Collaborate with Data Science,



Engineering, Media Strategy, and Business teams to translate analytical findings into marketing optimization strategies.
Build scalable Python-based analytics pipelines for model development, validation, monitoring, and reporting.
Present statistical findings and business recommendations to stakeholders with explicit explanations of assumptions, confidence intervals, and model limitations.

Required Skills

Experience
3–6 years of experience in Marketing Analytics, Marketing Science, Applied Data Science, Econometrics, or Media Analytics.
Robust experience working in agency, consulting, or digital marketing analytics environments.

Core Technical Skills
Expert knowledge of Marketing Mix Modelling (MMM).
Strong understanding of Bayesian Inference and Bayesian statistical techniques.
Solid expertise in Statistical Modelling including:
Linear Regression
Multivariate Regre

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