Data Scientist

Location: Noida, India

Department: PPC

Experience: 2-3 years

• • 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.

• • 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.

• • 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.

• • 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.

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