Data Scientist

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

• Experience: 3+ years
• Seniority: Mid Level
• Languages: English