Senior Data Scientist, Marketing and Personalization

What You Will Do

• 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

What You Bring to the Team

• 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
• Strong communication and collaboration skills

Bonus Points

• Experience with marketing analytics or customer lifecycle use cases
• Familiarity with experimentation and measurement (e.g., A/B testing, basic causal inference concepts)
• Experience working with cross-functional teams across Marketing, Product, and Engineering
• Experience with large-scale data processing tools (e.g., Spark)
• Track record of delivering business impact through data science projects