Data Scientist

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