Senior / Lead Analyst, Customer Insights

Arcadia

1d • $125,000 - $170,000 • Remote

About The Position

Arcadia is seeking a Senior/Lead Analyst, Customer Insights to join their Analytics organization. This role is central to transforming complex healthcare data into actionable insights for better outcomes and smarter decisions. The position involves building and owning productized analytics engines (risk, medical economics, episodes-of-care) and conducting deep-dive analysis for specific customer needs, including custom data models, bespoke dashboards, and advisory work. The role offers opportunities to work on various initiatives, such as a state Medicaid risk-stratification initiative, versatile project delivery for health systems and payers, and dedicated analytics support for a national health plan's Medicaid program. Successful candidates will combine technical excellence with influence over analytic strategy and product direction, shaping how data is used to improve population health, reduce costs, and enable smarter healthcare decisions. The specific responsibilities will vary depending on the placement, but all roles are at the Senior or Lead Analyst level.

Requirements

• Strong SQL and data modeling expertise — able to translate ambiguous business or clinical requirements into a concrete data plan, then build the underlying code and analysis on top of it.
• Healthcare data experience — claims and/or clinical data, with value-based care familiarity.
• Direct customer- or partner-facing experience — comfortable scoping and presenting without close oversight.
• Comfortable adopting AI tooling as a standard part of an analytical workflow.
• For Lead-level placement: demonstrated experience leading analytical work independently, managing multiple stakeholders, and mentoring less experienced analysts.
• Our experience bar across these roles generally skews toward Lead-level expectations, even for some Senior Analyst placements.

Nice To Haves

• Experience with dbt-based analytics engine development (e.g., risk, medical economics, episodes-of-care) and partnering with product/engineering on roadmap.
• Dashboarding experience — QuickSight or a comparable BI tool.
• Enough quantitative/ML fluency to build models and hand them to a Data Science team for training.
• Experience with medical economics, risk algorithms, quality data, and/or episodes-of-care analytics.
• Prior experience with state Medicaid programs or similar large government health programs.
• Prior experience in a project-based/retainer delivery model spanning multiple customers, rather than a single dedicated account.
• Experience working within a prime contractor or partner structure on shared delivery.
• Experience presenting analytic capabilities externally — customer meetings, industry conferences, publications.
• Python for analysis and QA; familiarity with the AWS ecosystem, Jira, and Confluence.

Responsibilities

• Translate ambiguous or partially-scoped business and clinical questions into a concrete, buildable data plan, then build the underlying data model, code, and analysis on top of it.
• Develop and maintain analytical pipelines and reusable data assets using SQL, dbt, and Arcadia's core data platform (Foundry, AWS data lake environments).
• For analytics-engine placements: serve as the lead analytics strategist for at least one of Arcadia's productized analytics engines (e.g., risk, medical economics, episodes-of-care), owning it end-to-end while contributing to others.
• For engagements involving predictive or risk models: design and build the models, partnering with a dedicated Data Science team on training and validation.
• Build dashboards, provider scorecards, registries, and other analytical deliverables (QuickSight and beyond) on top of that modeling work, as the engagement calls for it.
• Maintain the analytical integrity and quality of your work, flagging issues before they reach a customer or partner; for senior placements, oversee analytic quality standards and rigor across engagements more broadly, not just your own.
• Manage delivery — whether that's a single program's delivery calendar and decision log, or a portfolio of concurrent projects across multiple customers — surfacing risks and tradeoffs before they become blocking issues.
• Create client-facing materials — status decks, decision memos, technical explainers — tailored to a range of stakeholders, from highly technical counterparts to policy or business stakeholders with limited technical background.
• Delegate routine or repeatable work to junior analysts as your engagement grows, and help cross-train others for coverage.
• Lead cross-functional analytic initiatives that span multiple teams or engagements.
• Serve as a primary or supporting day-to-day contact for your engagement's customers or delivery partners, navigating scope and timeline conversations directly.
• Partner with Customer Management, Growth, Product, and/or Engineering — depending on placement — on account strategy, product direction, or analytics roadmap; for analytics-engine placements, partner closely with product and engineering to deliver Arcadia's analytics roadmap and contribute directly to product innovation.
• Hold real authority at the boundaries where your work meets someone else's — able to require rework or hold a handoff that isn't ready (e.g., with a partner Data Science team), or push back on what's actually worth building with Customer Management, Growth, or Product, rather than defaulting to whatever's asked.
• Act as an internal thought leader on your area of expertise, contributing to Arcadia's broader analytics strategy and reusable methodology — and, for some placements, an external one, representing Arcadia's analytic capabilities at customer meetings, industry conferences, and in publications.
• Mentor and develop junior analysts, building organizational capacity beyond your own individual delivery.
• Take ownership of your area's long-term health, not just its immediate delivery — a backlog/tech-debt log, a growing backup role on an account, or a roadmap for an analytics engine, depending on placement.
• Apply AI tooling as a standard part of your workflow — analysis, QA, documentation — not an occasional accelerant.
• Identify opportunities for automation, scalability, and reusability in analytical delivery, and help build those improvements out.

Benefits

• Flexible Time Off
• Competitive compensation
• Strong benefits
• Employee driven programs and initiatives for personal and professional development
• A flexible, remote-friendly company with personality and heart

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed