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 clear 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.
• Strong 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.

• Strong expertise in Statistical Modelling including:

• Linear Regression

• Multivariate Regression

• Hierarchical Models

• Time-Series Models

• Econometric Modelling

• Hands-on experience with Causal Inference methodologies such as:

• Difference-in-Differences

• Synthetic Control

• Propensity Score Matching

• Instrumental Variables

• Uplift Modelling

• Strong Python programming skills using:

• pandas

• NumPy

• SciPy

• scikit-learn

• PyMC / PyMC3

• Statsmodels

• Strong SQL skills.

• Experience with Power BI or Looker Studio.

Preferred Skills

• Experience with Google Meridian Marketing Mix Modeling Framework.
• Experience building Bayesian MMM models using Meridian.
• Knowledge of GeoLift, LightweightMMM, Robyn, or other modern MMM frameworks.
• Experience with GCP, BigQuery, Vertex AI, or cloud-based analytics platforms.
• Knowledge of MLflow, Airflow, Docker, and CI/CD.
• Familiarity with Generative AI for reporting automation and insight generation.

Must-Have Keywords for Screening

• Marketing Mix Modeling
• MMM
• Bayesian
• Bayesian Inference
• PyMC
• PyMC3
• Statistical Modeling
• Econometrics
• Causal Inference
• Incrementality
• Regression
• Statsmodels
• Meridian
• Google Meridian
• LightweightMMM
• Robyn