Principal, Marketing Data Science

Empower Pharmacy

1d•Onsite

About The Position

Empower Pharmacy is a visionary healthcare company dedicated to making quality, affordable medication accessible to millions of patients nationwide. As the nation’s most advanced 503A compounding pharmacy and FDA-registered 503B outsourcing facility, we’re redefining what’s possible in personalized medicine and pharmaceutical manufacturing. We’re proud to be recognized as one of Houston’s fastest-growing private companies and ranked #116 in Healthcare & Medical on the Inc. 5000 List for 2025. Our strength is built on four core values: People, Quality, Service, and Innovation. Guided by these principles, we’ve created a uniquely integrated healthcare platform powered by advanced technology, operational excellence, and a relentless commitment to patient care. From manufacturing and quality control to distribution and customer experience, our teams work together to raise industry standards, expand access to critical medications, and improve outcomes for patients and providers across the country. At Empower, joining our team means more than starting a new role. It means becoming part of a mission-driven organization that’s transforming healthcare at scale. We invest deeply in our people, encourage bold thinking, and create opportunities for growth, leadership, and innovation at every level. Your ideas matter here, your development is supported, and the work you do has a direct impact on the lives of millions. If you thrive in a fast-moving, purpose-driven environment where innovation, collaboration, and ambition come together, Empower Pharmacy is the place for you. Let’s transform healthcare together. 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

Requirements

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.
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.

Nice To Haves

Experience working within regulated industries, such as healthcare or financial services, where data privacy and compliance requirements shape technical decision-making is preferred.

Responsibilities

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.
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.
Conducts rigorous testing, validation, and performance monitoring of deployed models to ensure accuracy, stability, and continued predictive value as marketing conditions evolve.
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.
Partners with marketing technology and IT teams to integrate new data sources and algorithmic outputs into existing platforms, ensuring scalable, production-ready deployment.
Establishes data quality standards and monitoring processes to ensure marketing data feeding models remains accurate, complete, and reliable over time.
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.
Maintains ongoing analytics and reporting deliverables that track marketing performance, model impact, and business outcomes for stakeholders across the organization.
Collaborates with marketing, sales, and product teams to ensure model outputs are understood, adopted, and applied effectively in day-to-day decision-making.
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.
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.
Stays current on emerging machine learning techniques, marketing attribution methodologies, and regulatory developments relevant to healthcare marketing analytics.