Senior Data Scientist (MMM) ID71005

• What you will do

• Develop and scale client-specific Marketing Mix Modeling (MMM) models using Meridian or Robin rather than a single monolithic model;
• Build pragmatic baseline models to handle noisy marketing data and thin data tiers for new clients;
• Treat ML training as an operations problem by utilizing AWS tools to train and deploy models;
• Collaborate with internal MLOps teams to define the foundational infrastructure for deploying machine learning models to production.

• Must haves

• 5+ years of commercial data science experience with a proven track record of operating autonomously;
• Experience building and maintaining machine learning models, including choosing the approach, shaping the data, diagnosing problems, and iterating;
• Demonstrated experience with Bayesian frameworks such as PyMC or Stan , or Marketing Mix Modeling (MMM) tooling;
• Strong proficiency in Python and SQL ;
• Solid experience building and deploying models using AWS infrastructure such as SageMaker Studio or EC2 ;
• Strong foundation working with relational databases such as PostgreSQL or MySQL to pull, clean, and manipulate large data tiers;
• Upper-intermediate English level.

• Nice to haves

• Deep customization experience with Google Meridian ;
• Experience building or designing human-in-the-loop machine learning workflows;
• Exposure to cloud data lakes and analytical databases such as AWS Athena or ClickHouse ;
• Experience collaborating with backend engineering teams using containerized workflows or modern package ecosystems.

We are looking for a Senior Data Scientist to lead the implementation of a Marketing Mix Modeling framework into a cross-channel optimization product, building client-specific MMM models using Bayesian frameworks including Meridian and Robin. You will develop scalable, production-ready models using Python, SQL, and AWS infrastructure, design human-in-the-loop feedback systems to improve model accuracy over time, and collaborate with MLOps teams to define the foundational infrastructure for ML deployment. The role requires 5+ years of data science experience with autonomous, end-to-end model ownership.