Data Scientist

Bayesian and frequentist approaches to media mix modeling

Key components of the project include:

• Data Integration:

  Combining client first-party, third-party, and campaign-level data across digital, offline, and emerging channels into a unified modeling framework.

• Model Development:

  Building and validating media mix models (MMM) using advanced statistical and machine learning techniques such as hierarchical Bayesian regression, regularized regression (Ridge/Lasso), and time-series modeling.

• Scenario Simulation:

  Enabling stakeholders to forecast outcomes under different budget allocations through simulation and optimization algorithms.

• Deployment & Visualization:

  Using

  Streamlit

  to build interactive, client-facing dashboards for model exploration, scenario planning, and actionable recommendation delivery.

• Scalability:

  Engineering the system to support multiple clients across industries with varying data volumes, refresh cycles, and modeling complexities.

Responsibilities

• Develop, validate, and maintain

  media mix models

  to evaluate cross-channel marketing effectiveness and return on investment.
• Engineer and optimize

  end-to-end data pipelines

  for ingesting, cleaning, and structuring large, heterogeneous datasets from multiple marketing and business sources.
• Design, build, and deploy

  Streamlit-based interactive dashboards

  and applications for scenario testing, optimization, and reporting.
• Conduct

  exploratory data analysis (EDA)

  and advanced feature engineering to identify drivers of performance.
• Apply

  Bayesian methods, regularization, and time-series analysis

  to improve model accuracy, stability, and interpretability.
• Implement

  optimization and scenario-planning algorithms

  to recommend budget allocation strategies that maximize business outcomes.
• Collaborate closely with product, engineering, and client teams to align technical solutions with business objectives.
• Present insights and recommendations to senior stakeholders in both technical and non- technical language.
• Stay current with

  emerging tools, techniques, and best practices

  in media mix modeling, causal inference, and marketing science.

• Bachelor s or Master s degree in

  Data Science, Statistics, Computer Science, Applied Mathematics, or related field

  .
• Proven hands-on experience in

  media mix modeling, marketing analytics, or econometrics

  .