Sr Manager, Clinical Product Analytics

CVS Health•Work At Home-Texas, TX

2d•$118,450 - $236,900•Remote

About The Position

We are seeking an experienced Senior Manager, Clinical Product Analytics to lead the analytics function supporting our in-home and virtual evaluation products. This leader owns the measurement strategy for clinical encounter quality, diagnostic accuracy, and coding integrity — translating clinician, member, and claims data into the evidence base that drives product roadmap, clinical protocol design, and network performance decisions. As Senior Manager, you will lead a team of product and clinical analysts and partner closely with engineering, product, clinical operations, coding/HIM, quality, and compliance. You will set the standards for how the organization defines and measures a high-quality encounter, and you will be accountable for the analytics that senior leadership uses to steer the business.

Requirements

Bachelor's degree in a technical field (Statistics, Data Science, CS, Engineering, Epidemiology/Biostatistics, or related)
7-10 years of experience in product analytics, clinical analytics, or data science, including 2+ years directly managing analysts or data scientists
Demonstrated ownership of an analytics roadmap end to end, from stakeholder intake through delivery and adoption
Advanced SQL and working proficiency in Python or R
Experience with dbt (or similar analytics engineering frameworks) and Git-based development workflows
Experience with A/B testing and causal inference methods, and sound judgment about when each is appropriate
Experience partnering with data engineering teams on pipeline design and data quality
Ability to tell compelling stories through data and articulate technical information to non-technical and clinical audiences

Nice To Haves

Experience with healthcare data — claims, EHR/clinical documentation, HEDIS or Star Ratings measures, ICD-10-CM and HCC risk adjustment
Advanced degree in Public Health, Epidemiology, Biostatistics, Health Informatics, or a related field
Power BI experience at portfolio scale, including workspace governance and row-level security
Familiarity with advanced causal inference techniques (e.g., instrumental variables, synthetic control)
Clinical coding credential (CRC, CPC) or direct experience supporting coding quality, audit, or validation programs
Experience in value-based care, Medicare Advantage, or home-based care delivery

Responsibilities

Lead, mentor, and manage a team of product/clinical analysts, including hiring, goal setting, formal performance review, and technical career development
Own the definition and governance of the clinical product metric set — encounter completion and yield, documentation completeness, diagnostic specificity, condition capture and confirmation rates, care gap and screening closure, clinician productivity and quality scores, and product profitability
Set the standard for analytics engineering across the function: dbt models, semantic layer definitions, Git-based version control, testing, and documentation, so that a metric means the same thing in every dashboard, deck, and payer deliverable
Direct the Power BI portfolio for product health and clinician network performance; establish criteria for what earns a permanent dashboard versus what stays an ad hoc analysis
Set the causal inference standard for the function — govern experiment design where A/B testing is feasible and adjudicate method selection (Diff-in-Diff, propensity score matching, instrumental variables, interrupted time series) where it is not, particularly for clinical protocol and workflow changes that cannot be randomized at the member level
Build the evidence base for coding integrity and audit readiness, including coding pattern and outlier analysis, inter-rater and clinician variation analysis, and retrospective review of documentation supporting submitted diagnoses
Apply population health methods — cohort construction, prevalence and incidence estimation, risk stratification, burden-of-illness and social determinants analysis — to identify where encounters are and are not reaching the members who benefit most
Partner with data science and engineering on productionizing models into the point-of-care workflow (prospective condition suspecting, screening instrument prioritization, documentation prompts), owning the offline evaluation and post-deployment monitoring plan
Develop ROI projections and prioritize the analytics roadmap by business impact; manage resourcing, intake, and delivery commitments across competing product and clinical stakeholders
Advise senior leadership and enterprise partners on data-driven strategy, translating technical findings and their limitations into recommendations a non-technical executive audience can act on
Ensure analytics practices meet HIPAA, compliance, and regulatory expectations, and hold up under external scrutiny including audit and validation review