Senior Data Scientist, Marketing Science and Personalization

Fanatics Commerce•Redwood City, CA

4d•Hybrid

About The Position

Fanatics Commerce is seeking a Senior Data Scientist to drive Marketing Data Science. This role focuses on improving marketing effectiveness through data science and machine learning, working on key modeling areas such as purchase propensity, digital ad optimization, customer routing, incrementality, and personalization. The ideal candidate will have a strong background in applying data science to marketing or customer-focused problems, delivering actionable insights, and possess solid technical expertise, business intuition, and the ability to translate ambiguous problems into structured analyses and scalable solutions. This role involves close collaboration with stakeholders across Marketing, Product, Engineering, CRM, and Design.

Requirements

5+ years of hands-on experience in marketing data science or related applied data science domains, with demonstrated ability to build and deploy machine learning models in production environments.
Strong foundation in statistics and machine learning, including proficiency in Python and SQL and experience working with large-scale datasets.
Experience working with customer or marketing-related data across use cases such as personalization, segmentation, lifetime value modeling, or experimentation.
Ability to work independently on moderately complex problems, structuring ambiguous questions into actionable analyses and scalable model solutions.
Strong communication and collaboration skills, with the ability to present technical findings clearly to both technical and non-technical stakeholders.
Familiarity with experimentation and measurement methodologies, including A/B testing and foundational causal inference concepts.
Experience working cross-functionally with Marketing, Product, and Engineering teams to drive data science projects from problem definition through deployment.
BS or higher in a technical or quantitative field such as Computer Science, Statistics, Data Science, Mathematics or a related discipline.

Responsibilities

Partner closely with cross-functional stakeholders across Marketing, Product, Engineering, CRM, and Design to understand business problems and translate them into well-scoped data science solutions.
Contribute to team-level best practices in modeling methodology, code quality, and documentation.
Support and mentor junior team members through active collaboration, knowledge sharing, and constructive feedback.
Build, validate, and deploy machine learning and statistical models including propensity modeling, customer segmentation, and lifetime value estimation.
Work across marketing and customer domains, including personalization, lifecycle optimization, retention, and engagement.
Analyze large and complex customer datasets to identify trends, opportunities, and gaps in customer experience and marketing performance.
Apply appropriate experimentation and measurement techniques including A/B testing and causal inference to rigorously evaluate the fan and business impact of deployed models.
Contribute to the full end-to-end model development lifecycle, including data exploration, feature engineering, modeling, evaluation, and production deployment.
Develop scalable, production-ready solutions in collaboration with engineering teams.
Explore and apply large-scale data processing tools such as Spark to address the complexity and volume of Fanatics Commerce customer data.
Bring curiosity and structured thinking to ambiguous problem spaces, converting loosely defined business questions into rigorous analytical frameworks.
Own the quality, documentation, and communication of modeling outputs, ensuring findings and recommendations are clearly conveyed to both technical and non-technical audiences.
Manage project work across multiple concurrent initiatives with minimal oversight, delivering reliable results on moderately complex problems.
Demonstrate a track record of measurable business impact through data science work, taking accountability for outcomes rather than just outputs.
Apply AI and technology to improve efficiency, quality, and outcomes.
Use data and digital tools to inform decisions and enhance performance.
Demonstrate curiosity and adaptability in adopting new technologies and ways of working.
Contribute to a culture of innovation and continuous improvement.