Job Opportunities API

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