Principal, Marketing Data Science

Position Summary:

The Principal, Marketing Data Science builds the algorithmic and machine learning foundation that turns Empower’s marketing data into predictive, self-improving systems for growth. This role owns the design and development of attribution models, AI-driven learning systems, and the algorithmic infrastructure that processes and activates marketing data at scale, directly supporting patient access, provider adoption, and long-term value creation in a highly regulated healthcare environment. Traditional analytics and reporting remain part of the job, but the core of the role is hands-on model building: developing the systems and algorithms that let Empower’s marketing decisions get measurably smarter over time, not just measured after the fact

Responsibilities:

Model Development

• Model Building: Designs, builds, and continuously refines attribution and incrementality models as hands-on technical work, treating model architecture, validation, and retraining as core ongoing responsibilities rather than something delegated or outsourced.
• AI Systems: Develops and deploys AI and machine learning systems, such as propensity models, predictive forecasting, and automated media optimization logic, that learn continuously from marketing and engagement data to drive decisions rather than just describe past performance.
• Model Testing: Conducts rigorous testing, validation, and performance monitoring of deployed models to ensure accuracy, stability, and continued predictive value as marketing conditions evolve.

Data & Infrastructure

• Pipeline Architecture: Architects the algorithmic pipelines and data infrastructure that ingest, process, and operationalize marketing data at scale, partnering with marketing technology and IT to build systems and automated workflows, not just dashboards and reports.
• Systems Integration: Partners with marketing technology and IT teams to integrate new data sources and algorithmic outputs into existing platforms, ensuring scalable, production-ready deployment.
• Data Quality: Establishes data quality standards and monitoring processes to ensure marketing data feeding models remains accurate, complete, and reliable over time.

Stakeholder Communication

• Executive Guidance: Translates model logic, assumptions, and outputs into clear, executive-level guidance, ensuring leadership understands not just what a model concludes but how and why it works, while maintaining the traditional analytics and reporting this leader is also responsible for.
• Performance Reporting: Maintains ongoing analytics and reporting deliverables that track marketing performance, model impact, and business outcomes for stakeholders across the organization.
• Cross-Functional Alignment: Collaborates with marketing, sales, and product teams to ensure model outputs are understood, adopted, and applied effectively in day-to-day decision-making.

Governance & Compliance

• Technical Authority: Serves as the technical authority on marketing data science, aligning model development with product, sales, operations, and compliance priorities, and ensuring all algorithmic and AI work meets HIPAA and healthcare data privacy requirements.
• Compliance Oversight: Reviews algorithmic and AI workflows on an ongoing basis to confirm continued alignment with HIPAA and healthcare data privacy standards as models and data sources evolve.
• Industry Awareness: Stays current on emerging machine learning techniques, marketing attribution methodologies, and regulatory developments relevant to healthcare marketing analytics.

Knowledge and Skills:

• Hands-on fluency in Python or R and SQL, with experience building, validating, and productionizing predictive or causal models rather than relying solely on BI or visualization tools.
• Working knowledge of marketing attribution methodologies, causal inference and incrementality techniques, and applied machine learning (regression, classification, time series, propensity modeling), with the strategic thinking and communication skills to translate that work for senior stakeholders and ensure compliance with healthcare data privacy requirements.
• Strong understanding of statistical modeling techniques and experimental design principles used to validate causal and predictive relationships in marketing data.
• Ability to communicate complex technical concepts clearly to non-technical stakeholders and translate analytical findings into actionable business recommendations.

Experience and Qualifications:

• A minimum of 5 years of experience building attribution models, machine learning systems, or algorithmic data products within complex, multi-channel marketing environments, with direct hands-on model development experience.
• Requires a bachelor’s degree or equivalent work experience in data science, statistics, computer science, applied mathematics, or a related quantitative field; an advanced degree in a quantitative discipline is a plus.
• Proven track record of building and deploying models into production marketing environments, with measurable impact on campaign performance or business outcomes.
• Experience working within regulated industries, such as healthcare or financial services, where data privacy and compliance requirements shape technical decision-making is preferred.