Staff Analyst, Product Analytics

CVS Health•Work At Home-Texas, TX

3d•$118,450 - $236,900•Remote

About The Position

We are seeking a Staff Analyst, Product Analytics to serve as the senior individual contributor and technical authority for the analytics that drive member engagement, appointment scheduling, and clinician capacity utilization. This role owns the hardest analytical problems in the funnel — why members can and cannot be reached, what channel and cadence actually moves a member to schedule, and how appointments should be sequenced against a constrained and geographically distributed clinician network. As a Staff Analyst, you will operate at the same level of scope and impact as a Senior Manager but through technical depth and influence rather than direct reports. You will set methodological standards, mentor analysts across the function, and partner directly with product, engagement operations, clinical network, data science, and engineering.

Requirements

Bachelor's degree in a technical field (Statistics, Data Science, CS, Engineering, Economics, Epidemiology/Biostatistics, or related)
7-10 years of experience in product analytics, data science, or quantitative marketing/engagement analytics
Demonstrated ownership of a major analytical domain end to end, including setting definitions others depend on
Advanced SQL and strong proficiency in Python or R
Experience with dbt (or similar analytics engineering frameworks) and Git-based development workflows
Deep experience with experimental design and causal inference, including sound judgment about when each method is appropriate and where it fails
Experience partnering with data engineering teams on pipeline design and data quality
Track record of influencing technical direction and mentoring analysts without formal management authority
Ability to tell compelling stories through data and articulate technical information to non-technical and operational audiences

Nice To Haves

Experience with contact center, outbound dialer, CRM, or marketing automation data (e.g., Genesys, Five9, Twilio, Salesforce)
Experience with survival analysis, uplift/heterogeneous treatment effect modeling, or multi-armed bandit approaches to channel and cadence optimization
Experience with routing, capacity, or constrained optimization problems
Advanced degree in Public Health, Epidemiology, Biostatistics, Economics, or a related quantitative field
Power BI experience at portfolio scale, including workspace governance and row-level security
Experience in value-based care, Medicare Advantage, or home-based care delivery
Familiarity with TCPA and member contact compliance constraints as they affect measurement design

Responsibilities

Own the analytical model of the outreach-to-completed-encounter funnel end to end: list availability, contactability and right-party contact, connect and conversion rates, appointment scheduling, cancellation and no-show behavior, reschedule recovery, and final encounter completion
Serve as the technical authority for channel and cadence strategy — quantifying the marginal value of each additional attempt, the substitution effects between outbound call, SMS, email, IVR, direct mail, and digital self-scheduling, and the point at which continued outreach produces diminishing or negative returns on member experience
Design and interpret experiments on outreach treatments (contact cadence, script and messaging variants, incentive structures, self-scheduling flows); where randomization is not feasible, select and defend the appropriate quasi-experimental method (Diff-in-Diff, propensity score matching, instrumental variables, interrupted time series, regression discontinuity on assignment thresholds)
Build and own the analytics engineering layer for engagement data in dbt — contact attempt event models, member-level funnel state, agent and vendor performance, appointment lifecycle — with version control, testing, and documentation sufficient for other teams to build on without re-deriving definitions
Partner with data science on propensity-to-schedule and contactability models: define the target variable, own the offline evaluation and fairness assessment, and design the operational test that determines whether the model beats current list prioritization in production
Analyze scheduling density, appointment lead time, geographic coverage, and clinician capacity utilization to identify where supply and demand are misaligned, and quantify the tradeoff between routing efficiency and member scheduling preference
Measure engagement equity across member populations, applying population health and SDOH frameworks to identify which cohorts — rural, dual-eligible, limited English proficiency, no reliable phone, high social vulnerability — are systematically under-reached, and size the closable gap
Build and maintain the Power BI reporting that engagement operations and product leadership run the business on, with clear separation between operational monitoring and analytical deep dives
Develop ROI and capacity models that translate outreach investment, agent hours, and vendor spend into projected completed encounters, and use them to prioritize the analytics and product roadmap
Set methodological standards for the broader analytics function and mentor analysts on experimental design, causal inference, and analytics engineering practice without formal reporting authority
Advise senior leadership on engagement strategy, translating technical findings and their limitations into recommendations executives and operations leaders can act on
Ensure outreach analytics reflect TCPA, HIPAA, and member contact-preference constraints, and that measurement of agent and vendor performance holds up under compliance and audit review