The Role
Lead development and productionization of client-specific Marketing Mix Models using Bayesian frameworks. Build robust baseline models for noisy or thin data, design human-in-the-loop workflows to improve accuracy, and collaborate with MLOps to deploy models on AWS infrastructure (SageMaker/EC2). Own end-to-end modeling, data shaping, diagnostics, and iteration.
Summary Generated by Built In
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
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.
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.
PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.
Skills Required
- 5+ years of commercial data science experience with autonomous, end-to-end model ownership
- Experience building and maintaining machine learning models including model selection, data shaping, diagnosing problems, and iteration
- Demonstrated experience with Bayesian frameworks such as PyMC or Stan, or Marketing Mix Modeling (MMM) tooling
- Strong proficiency in Python
- Strong proficiency in SQL
- Experience building and deploying models using AWS infrastructure (SageMaker Studio or EC2)
- Experience working with relational databases (PostgreSQL or MySQL) to pull, clean, and manipulate large data tiers
- Upper-intermediate English level
- 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
Am I A Good Fit?
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The Company
What We Do
AgileEngine is a privately held company established in 2010 that builds dedicated teams of designers and developers. We turn good ideas into awesome software that people actually want to use. Some of the biggest names and the hottest startups around the world chose us to build their tech.
