Manager, Data Strategy & Analytics

Reimagine Care•,

19h•Remote

About The Position

Reimagine Care is a virtual oncology care management company that partners with health systems to extend cancer care beyond the clinic walls. Through a combination of AI-driven patient engagement, virtual care coordination, and clinician-led support, we help oncology programs improve patient outcomes, reduce avoidable acute care utilization, and build the infrastructure for value-based oncology contracting. Our platform captures a rich, longitudinal view of the between-visit cancer patient experience, including patient-reported symptoms, AI-mediated interactions, clinical escalations, engagement patterns, and care utilization. The Manager, Data Strategy & Analytics, is responsible for the day-to-day delivery of analytics at Reimagine Care. This role develops and maintains the measurement and reporting needed to demonstrate program value to clients, support internal operational decisions, monitor AI quality, and enable commercial growth. This is a hands-on individual-contributor role. The right candidate will be comfortable writing complex SQL, developing KPI frameworks, building Tableau dashboards, conducting outcomes analyses, and preparing clear materials that support commercial growth and client and executive reporting. They should also understand how a virtual oncology company can responsibly use its data to create value for providers, life sciences, and the broader industry. The work spans multiple domains and requires someone who can move between analytical problem-solving and technical execution with equal ease. Based on business needs and the selected candidate’s experience, the role may be leveled at Director with a corresponding expansion in strategic, client-facing, and functional leadership responsibilities.

Requirements

5+ years of experience in healthcare analytics, health system strategy, population health, or a closely related field
Experience managing complex analytics projects or workstreams
Demonstrated experience building outcomes measurement frameworks and developing performance reporting for health system or executive audiences
Strong SQL skills with the ability to write, validate, and maintain complex production queries against relational databases
Proficiency with an enterprise business intelligence platform; Tableau experience is strongly preferred
Working knowledge of healthcare economics: fee-for-service vs. value-based reimbursement, payer contracting, acute care cost structures, and models like the Enhancing Oncology Model
Experience in designing and interpreting quantitative research in applied settings (cohort studies, pre/post comparisons, survival analysis, survey analysis)
Ability to translate complex data into clear findings and recommendations for nontechnical audiences
Comfort working independently and managing multiple priorities in a growth-stage environment

Nice To Haves

Experience in oncology care delivery, or cancer center operations
Experience with healthcare data monetization, real-world evidence (RWE) generation, or partnerships with pharmaceutical or life sciences organizations
Familiarity with AI/ML model monitoring, LLM evaluation, quality governance, or clinical decision-support safety frameworks
Experience with Databricks, cloud data platforms, or modern analytics engineering workflows
Experience building ROI models, financial impact analyses, or value-based contracting analytics for health system or payer buyers
Exposure to predictive or prescriptive analytics use cases in healthcare (risk stratification, intervention targeting, resource optimization)

Responsibilities

Design and maintain KPI frameworks for clients covering enrollment, engagement, acute care utilization, symptom burden, patient & provider experience, and AI safety
Deliver recurring monthly and quarterly performance reports and prepare analyses and materials that support executive and board-level reporting
Conduct outcomes analyses using methods appropriate to the data: cohort comparisons, pre/post designs, survival analysis, cost avoidance modeling, and longitudinal symptom tracking (e.g., ESAS trajectories)
Partner with Reimagine Care’s Client Success team and client stakeholders to define success criteria, align methodologies and comparison cohorts, and establish reporting cadence
Participate in client-facing meetings, Business Reviews, and Joint Operating Committee presentations by providing analysis, data interpretation, and supporting materials
Collaborate with Data Engineering, Product, and Engineering on analytics workflows across Azure, Databricks, and Tableau and support improvements as the company’s data needs evolve
Build and maintain validated source-of-truth queries for critical metrics: enrollment, engagement, retention, and cohort classification
Develop scalable reporting workflows and self-service analytics capabilities so internal teams can answer routine questions without custom query work
Manage an analytics request intake process that triages, prioritizes, and tracks work across stakeholders
Maintain data quality standards, documentation, and lineage across production schemas
Build and maintain ROI models and financial impact analyses, and translate findings into clear, data-supported materials for prospective clients
Develop prospect-facing materials grounded in validated program data, including utilization reduction estimates and cost avoidance projections
Support new product lines, including SaaS licensing of the company’s platform, with KPI libraries, benchmarking frameworks, and proof-of-value collateral
Partner with marketing and sales teams to ensure data claims in external materials are accurate, defensible, and sourced
Contribute analytics framing and evidence strategy for value-based care contracting conversations
Manage the analytics and reporting components of performance monitoring and quality governance for the company’s AI-driven care platform, including dashboards, KPI definitions, and analysis
Develop and track AI safety and accuracy metrics in partnership with Clinical, Product, and Technology stakeholders, including confusion matrix-derived indicators and clinician review outcomes
Produce regular AI quality reports for internal leadership and client-facing audiences and prepare supporting materials for board reporting
Identify opportunities to apply predictive and prescriptive analytics to clinical and operational problems: risk stratification, early intervention targeting, resource allocation, and care pathway optimization
Evaluate and pilot the use of large language models (LLMs) and other emerging AI capabilities to augment the analytics function itself, such as automated insight generation and natural language querying
Coordinate analytics priorities and deliverables in alignment with the company’s client portfolio, product roadmap, and business needs
Prepare structured updates and ad hoc analyses to support executive leadership decision-making
Work cross-functionally with Product, Engineering, Clinical Operations, and Commercial teams to embed analytics into decision-making across the organization
Engage with external data vendors, care partners, and research collaborators to support the organization's analytics and reporting needs