Senior Data Analyst

Guidehealth•Boston, MA

10h•$90,000 - $100,000•Remote

About The Position

As Senior Data Analyst, you will play a critical role in Guidehealth's mission to improve healthcare for all by supporting the data needs of our clinical, operational, and leadership teams and the clients we serve. You will gather requirements for reports and analyses, then determine the best approach to meet them. You will have access to a wide array of data sources, from traditional healthcare systems to AI-enabled tools, to build incisive analyses that stand up to scrutiny. You will present your work to internal and external audiences to drive better decision making. And your contributions to Guidehealth's data-driven clinical programs have the potential to positively impact the lives of every individual we serve.

Requirements

Bachelor's degree plus at least three years of professional experience in data analytics, or a master's degree with independent research experience.
Evidence of strong problem-solving instincts: curiosity about why the numbers look the way they do, and persistence in chasing down the answer.
Demonstrated experience performing analytics on healthcare data, with working knowledge of standard healthcare code sets and data structures (e.g., ICD-10, CPT/HCPCS, DRG, NDC, revenue codes, place of service, claim file layouts).
Strong SQL skills, including the ability to work with large, imperfect datasets and to write queries others can read and maintain.
Proficiency in at least one analytic or statistical tool beyond SQL (e.g., Python, R).
Experience building data visualizations and dashboards in a modern BI tool (e.g., Power BI, Tableau, Quicksight).
Track record of delivering work that survives past handoff, built and documented to be maintained, iterated on, and supported over time.
Excellent written and verbal communication, including experience presenting analytic results directly to stakeholders or clients.
Composure and professionalism in demanding situations, including evolving requirements, compressed timelines, and difficult conversations.

Nice To Haves

Direct experience with value-based care arrangements such as shared savings, capitation, bundled payments, or ACOs.
Experience using the AWS and/or Arcadia Analytics ecosystems.
Hands-on work with quality measure specifications and reporting (HEDIS, eCQMs, MIPS, Stars, state Medicaid measure sets).
Familiarity with risk adjustment methodologies (CMS-HCC, HHS-HCC, CDPS, or similar) and their analytic implications.
Experience with utilization and cost metrics such as admits/1,000, ED visits/1,000, readmission rates, PMPM, and total cost of care.
Exposure to interoperability and data exchange standards (HL7, FHIR, X12 837/834/835, CCDA).
Clinical, health policy, payer, or provider operations background.

Responsibilities

Handling a wide-ranging portfolio of analytic needs across internal teams and external clients, from quick-turn questions to sustained, multi-month engagements.
Working with healthcare data, including claims, eligibility/enrollment, clinical, and supplemental sources, to produce reliable, well-documented analytic datasets.
Designing, building, and delivering analyses that answer real business and clinical questions, from initial scoping through final presentation.
Owning custom client builds end-to-end: setting expectations, defining scope, developing, iterating based on feedback, and maintaining the deliverable over its lifespan.
Developing analyses supporting value-based care programs, including quality measure performance, risk adjustment and risk score accuracy, and utilization and cost-of-care trends.
Supporting a range of activities for assigned clients, serving as a reliable analytic point of contact and building trust through responsiveness and follow-through.
Validating results and investigating anomalies, treating unexpected findings as something to explore and explain rather than smooth over.
Balancing competing priorities across accounts and internal initiatives, setting realistic expectations on scope and timeline and communicating proactively when either needs to shift.
Communicating findings to technical and non-technical audiences through written summaries, live presentations, and clear visualizations that lead with the insight rather than the method.
Documenting methodology, assumptions, and known data limitations so work is reproducible, defensible, and transferable to another analyst.