Principal Decision Scientist, Strategy & Innovation

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

The Signify Health Strategy & Innovation team is responsible for shaping the company's long-term strategy, discovering opportunities to accelerate growth, and identifying and testing net new offerings or capabilities. The team serves in an advisory capacity to Signify's Executive Leadership Team (ELT) - partnering closely with leaders to evaluate challenges and opportunities, facilitate decision-making, and execute early-stage proofs of concept.

The Principal Decision Scientist is the team's first dedicated quantitative hire to bring additional rigor to strategy as an individual contributor who designs and builds the causal inference studies and sizing analyses that underpin Signify's highest-visibility strategic recommendations.

Work falls into two buckets: (i) leading deep statistical studies to prove member outcomes and (ii) providing quantitative sizing to guide decisions on new growth opportunities. The individual will also (iii) be responsible to collaborate across Signify's business functions, including with other data professionals to standardize and improve technical fundamentals, documentation, code reviews, data models, and quality controls.

Ideal candidates are data-driven, inquisitive, autonomous, and entrepreneurial thinkers who can collaborate effectively across functions and with executive leadership.

(i) Clinical Evidence Generation & Member Insights

• Extract knowledge and insights from data in order to investigate complex business problems through a range of data preparation, modeling, analysis and/or visualization techniques, including predictive analysis, business intelligence, pattern recognition, operational effectiveness and/or economic forecasting
• Design, build, and maintain causal inference studies that quantify the member-level clinical outcomes of the in-home health evaluation, supporting workflow and service enhancements
• Build and validate proof of concepts to identify rich, incremental member insights, leveraging health plan data combined with Signify's in-home insights. Potential approaches include statistical analysis, machine learning, and other applied methods

(ii) Analytic Support of Strategic and Growth Evaluations

• Translate ambiguous, open-ended business questions into structured analytical approaches, partnering with strategy and business function leaders
• Conduct new product and partnership opportunity sizings by analyzing Signify's internal operational and clinical data, supplemented by publicly available data where needed

(iii) Analytic Best Practices and Cross-Company Collaboration

• Champion analytics best practices, e.g. engineering standards, version control, code review, documented pipelines, common data sources, with other analytic leaders within Signify to improve quality, repeatability, and ability to build off each other's work
• Act as a technical thought partner and mentor to analytics talent across the organization
• Bring a creative, structured approach to pressure-testing methodology and hypotheses across the full active portfolio, not only assigned work streams
• Present analysis directly to Signify's ELT and executive leadership, as workstream lead or in support of a strategy lead, building credibility and influence at the executive level

• 10+ years of hands-on experience in Decision Science, Data Science, Advanced Analytics, or a related quantitative field, with ownership of complex analytical problems from problem definition through actionable recommendations.
• Expert SQL and strong Python skills, with experience analyzing large, complex datasets and applying statistical analysis, modeling, automation, and visualization.
• Strong applied statistics and advanced analytics expertise, including hypothesis testing, regression, experimental design/A-B testing, predictive modeling, and related quantitative methods.
• Demonstrated experience with causal inference and impact measurement to evaluate programs, interventions, strategies, or business decisions.
• Healthcare/payer data expertise, including hands-on experience…