Principal Analytics Analyst, Data Office

Description

About the Team

Do you enjoy stakeholder partnership, data craftsmanship, and delivering when it matters most? The US Retail Markets Data Office helps make data a competitive advantage for Liberty—by building trusted data products, improving quality and governance, and enabling smarter decisions across the enterprise. Within the Data Office, our Cross‑Domain Data Solutions team partners closely with Corporate Legal, Compliance, and Claims leaders to deliver  impactful claims analytics and insights that support regulatory requests, litigation support, and internal compliance monitoring. We aim to be trusted analytical partners who help our stakeholders not just get data, but understand it.

 

The Role (What you’ll do)

As a Principal  Analytics Analyst / Senior  Analytics Analyst, you’ll be a key contributor on a small, high‑impact team that turns complex  legal and regulatory questions into clear analytical requirements and high‑quality, insight-driven deliverables. You’ll  go beyond producing extracts and reporting to conduct in-depth analysis, with claims data. This is a great role for someone who enjoys a mix of forensic problem solving, stakeholder partnership, and hands-on data analysis, and who takes pride in delivering  insights that stand up to scrutiny.

 

**This position may have in-office requirements depending on candidate location.**  

 

Key Responsibilities

• Conduct deep-dive analysis of complex claims data to identify trends, anomalies, and key insights that directly inform legal strategy, regulatory responses, and compliance assessments.
• Partner with Legal & Compliance stakeholders to clarify request intent, scope, timelines, and success criteria.
• Adapt communication style appropriately to share project results, opportunities, and successes with both technical and business (non-technical) leaders.
• Develop and deliver claims  analytical solutions, including everything from foundational claim-level extracts to statistical summaries, predictive metrics, and interactive dashboards using tools like SQL/Snowflake, SAS Viya, Python, and other BI tools as needed.
• Prioritize efforts to meet delivery timelines, proactively communicating risks to stakeholders.
• Apply strong data QA practices (reconciliations, trend analysis, reasonableness checks,  peer review) to ensure both the data and the resulting analysis are accurate and defensible.
• Proactively identify opportunities for analysis; partner with stakeholders to move beyond reactive data pulls to a more forward-looking, analytics-driven approach.
• Produce and maintain clear documentation (methodology, definitions, assumptions/limitations, data lineage) so deliverables are reproducible and audit-ready.
• Help improve team efficiency by creating reusable code modules, templates, and standardized “request packages” for recurring stakeholder needs.
• Collaborate with data product and engineering partners to identify high-value recurring requests that should be supported by curated data assets.
• Demonstrates an understanding of new technologies as needed to progress initiatives, including leveraging approved AI tools to improve processes (within guardrails).
• Participate in the team’s operating cadence (standups, intake/triage, prioritization) and proactively communicate status, risks, and blockers.
• Acts as a technical guide and mentor for lower level associates.
• Embrace Diversity, Equity and Inclusion culture creating long-lasting and trusting relationships with teammates.

What Success Looks Like:

• Stakeholders experience you as a responsive, clear communicator who can translate ambiguity into actionable requirements.
• Deliverables are accurate, consistent, and well-documented, with fewer last-minute surprises or rework cycles.
• Effectively balances tasks simultaneously and adapts to shifting/competing priorities in a fast-paced working environment.
• Under limited direction, prototypes/develops data solutions of high complexity to meet the needs of the organization and business customers.
• Over time, you help create repeatable, scalable solutions through templates, automation, and better data assets.

Preferred / Nice to Have:

• 3–5+ years of professional experience in data analytics, with a strong preference for experience in the property and casualty insurance industry.
• Strong working knowledge of SQL and experience working with large datasets (Snowflake or similar platforms preferred).
• Proven experience in claims analytics, with a strong understanding of claims data concepts (e.g., claim/exposure/transaction grai