Principal Decision Scientist, Strategy & Innovation

Job description

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, reporting to the Vice President of Innovation, 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) (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. Position Responsibilities (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 workstreams • 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 Required Qualifications Decision / Data Science Technical Requirements • SQL: expert-level, hands-on querying and manipulation of large healthcare datasets; ability to size new offerings leveraging internal databases • Python: hands-on use for statistical analysis, modeling, automation, and exploratory / explanatory data visualization • Statistics: strong grounding in applied statistics, hypothesis testing, regression, distributional reasoning, sufficient to defend methodology choices • Causal inference: demonstrated experience applying causal inference methods to real business decisions • In addition to other relevant data analysis techniques (e.g., A/B testing, association rule learning, cluster analysis, pattern recognition and predictive modeling), data analysis and visualization software, object-oriented programming languages (Python, Java, R, Ruby, Scala, C++) and/or data engineering skills (relational or NoSQL databases, ETL tools) • Experience working with Snowflake (primary platform) and/or other cloud platforms (e.g. GCP, AWS, Asure) Additional Required Experience • 10+ years of experience in applied quantitative analysis, decision science, or data science, with direct, hands-on ownership of analyses (including academia but with a preference for industry experience) • Healthcare industry experience with hands-on data experience: notably with health plan payer data (e.g. claims, lab data, enrollment, SDOH, etc.). Experience with unstructured provider clinical data is preferred • Track record of introducing or materially improving analytics best practices • Executive presence: demonstrated ability to present complex, technical analysis directly to senior executive audiences and build credibility and influence at that level • Strong written communication skills, with experience producing analysis that non-technical stakeholders can act on without the author present to narrate it. Preferred Qualifications • Familiarity with CMS risk adjustment and STARS quality methodologies • Experience building and validating machine learning models or proof-of-concepts (e.g., predictive risk models, member insight models) as a complement to causal inference methods • Demonstrated comfort