Staff Analyst

About the role

We are looking for an ambitious, highly analytical self-starter to step into a high-visibility role within our Agent (Partnerships) business. Partnerships is our high-velocity business line that provides insurance agents with a digital-first platform, automated underwriting infrastructure, insurance products, and specialized services to help them sell more life insurance, faster.

Working as an elite individual contributor, you will sit squarely at the intersection of product mechanics, data science, and operational strategy. You will partner directly with our business leaders to solve unstructured data puzzles, proactively flag macro financial risks, and define the behavioral milestones that turn new agents into long-term platform advocates. If you are a big-picture thinker who loves digging into the data weeds to write the strategic story of enterprise growth, let's talk.

Duties and Responsibilities:

• Strategic Partnerships Data Lead: Serve as an analytics thought partner and data expert for the Partnerships leadership team, spearheading strategic decisions around agent acquisition, growth, retention, persistency, and more. 
• Core Product Analytics: Track, evaluate, and optimize the agent-facing platform experience. Define precision usability metrics for new product features to ensure engineering sprint cycles drive positive user actions.
• Operational Risk & Commission Debt Architecture: Build diagnostic data models to analyze, track, and mitigate backend financial exposure. Slice historical operational datasets to pinpoint risk concentration hotspots within external agencies and design targeted debt recovery frameworks.
• Strategic Input Modeling: Isolate critical growth levers across the agent journey to define the definitive onboarding metric (the "aha moment") that yields a positive snowball effect on platform retention and aggregate revenue generation.
• Funnel Optimization: Manage performance frameworks and monitor conversion steps for our emerging annuities business lines using a classic Direct-to-Consumer (D2C) funnel approach.
• Data Democratization & Infrastructure: Collaborate with data engineers and broader analytics teams to maintain scalable business logic, deploy clean tracking dashboards, and enable self-service analytics across the organization.
• Tooling Innovation: Actively build, test, and integrate advanced generative AI tools to automate internal analytics processes and maximize delivery speed.
• Team Mentorship: Provide ongoing guidance, peer code reviews, and tactical leadership training to distributed junior analytics resources across our global offices.

Qualifications and Skills:

• Experience Baseline: 5 to 8 years of experience in product analytics, business intelligence, data science, or analytically intensive individual contributor roles at high-growth tech startups or well-established firms.
• Technical Mastery: Non-negotiable expert knowledge of SQL. Strong foundational knowledge of statistics and predictive data modeling is highly desired. Familiarity with scripting languages (Python or R) is a major plus.
• AI-Native Workflow Fluency: Proven history of actively leveraging modern Large Language Models (like ChatGPT or Claude) inside your day-to-day workflow to streamline writing, debug calculations, and automate personal efficiency. Must rank in the top 10% of AI-fluent analysts.
• Analytics Stack Exposure: Experience utilizing advanced visualization, dashboarding, and AI-enabled analytics environments (such as Hex, Tableau, Mode, or Excel).
• Unstructured Problem Solving: Exceptional talent for entering highly ambiguous situations, slicing past data records proactively to expose root causes, and organizing disparate facts into clear corporate narratives.
• Executive Presentation Agility: Superior written and verbal communication skills; comfortable defending high-stakes analytical summaries to the corporate C-suite and Board of Directors.
• Education: Advanced degree in a quantitative domain (Statistics, Economics, Computer Science, Engineering, Applied Math) or equivalent practical technical background.

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