Principal Decision Scientist, Strategy & Innovation

CVS Health•Washington, NV

3d•$144,200 - $288,400

About The Position

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.

Requirements

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 with claims, enrollment, utilization, laboratory, SDOH, or other member-level healthcare datasets.
Strong business and executive communication skills, with the ability to translate ambiguous business problems into structured analytical approaches, actionable recommendations, and influence stakeholders in a matrixed environment.
Experience with Snowflake and/or modern cloud data platforms such as AWS, Azure, or GCP.
Working knowledge of data engineering and scalable analytical datasets, including ETL/ELT and relational or NoSQL databases.
Experience establishing or improving analytics/Decision Science best practices and developing reusable analytical assets.
Demonstrated executive presence and strong written communication, with the ability to clearly communicate complex analysis to senior and non-technical audiences.

Nice To Haves

Familiarity with CMS risk adjustment and Stars quality methodologies.
Experience building, validating, or deploying machine learning models or proof-of-concepts, including predictive risk models, member insight/segmentation models, or other decision-support applications.
Experience using machine learning and predictive modeling in combination with causal inference and other analytical methods to inform business decisions.
Experience working with unstructured clinical and/or provider data.
Experience applying decision science to complex healthcare, payer, provider, or healthcare operations problems.
Experience operating in highly ambiguous environments and independently defining the analytical strategy for novel or poorly defined business problems.

Responsibilities

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
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
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