Design, build, and deploy Marketing Mix Models and Bayesian statistical models to measure marketing effectiveness, forecast, and inform budget allocation. Apply causal inference and experimentation, analyze large marketing datasets, build Python analytics pipelines, and produce dashboards in Power BI or Looker Studio. Communicate findings and recommendations to stakeholders.
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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