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
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