Work on data science projects across marketing and customer domains, including personalization, lifecycle optimization, retention, and engagement
Build, validate, and deploy machine learning and statistical models for use cases such as propensity modeling, segmentation, and LTV
Partner with cross‑functional stakeholders (Marketing, Product, CRM, Engineering) to understand business problems and translate them into data science solutions
Contribute to the end‑to‑end model development lifecycle, including data exploration, feature engineering, modeling, evaluation, and deployment
Analyze large and complex datasets to identify trends, opportunities, and areas for improvement in customer experience and marketing performance
Apply appropriate statistical methods and experimentation techniques to evaluate model performance and business impact
Collaborate with engineering teams to ensure models are production‑ready and integrated into downstream systems
Communicate findings and recommendations clearly to technical and non‑technical stakeholders
Contribute to team‑level best practices in modeling, code quality, and documentation
Support and mentor junior team members through collaboration and knowledge sharing
Requirements
BS or higher in a technical or quantitative field (Computer Science, Operations Research, Statistics, Economics, etc.)
5+ years of hands‑on work in marketing data science or related areas
Experience building and deploying machine learning models in production environments
Strong foundation in statistics and machine learning
Proficiency in Python and SQL, with experience working on large datasets
Experience working with customer or marketing‑related data (e.g., personalization, segmentation, LTV, or experimentation)
Ability to work on moderately complex problems with some guidance