Senior Product Analyst

Assured•Palo Alto, CA

1d•$155,000 - $175,000•Remote

About The Position

Assured is on a mission to modernize insurance. Claims processing (i.e. should we pay this claim?), while often overlooked, is the foundation of the entire industry. It’s currently highly manual, involving phone calls, faxes, and gut instinct, costing tens of billions of dollars a year. We can do better. At Assured, we provide large insurers with the software solutions they need to win in a modern, technology-driven world. From self-service claim-filing software to backend fraud detection, we’re the engine that powers claims processing for some of the largest insurers in the world. The challenges we face are deep and diverse, from creating digital experiences that provide comfort and clarity to claimants at their most stressed and vulnerable to orchestrating large-scale ML-driven decision-making on billions of dollars of claims payments, life at Assured is dynamic, collaborative, and rewarding. Great products aren't built on intuition alone – they're built on understanding how customers actually use them. As a Senior Product Analyst, you'll help our Product and Engineering teams make better decisions by turning product data into meaningful insights. As we continue to expand AI and agent-powered capabilities across our platform, you'll play a key role in defining how we measure success, evaluate new experiences, and ensure product decisions are grounded in data. The best analysts don't just report what happened – they help teams decide what to do next. That's the kind of impact we're looking for in this role.

Requirements

Use data to drive decisions. You have a track record of answering complex product questions, designing meaningful analyses or experiments, and influencing what gets built next.
Have strong analytical fundamentals. SQL is a core part of your toolkit, and you're comfortable working with product data to uncover meaningful patterns and insights.
Communicate with clarity. You can explain analytical findings to both technical and non-technical audiences and help teams make informed decisions.
Enjoy solving ambiguous problems. You're comfortable starting with a question—not a predefined dataset—and figuring out what needs to be measured, analyzed, or built to find the answer.
Learn tools quickly instead of defining yourself by them. We use AI throughout our workflows and regularly experiment with tools like Cursor, Claude Code, Codex, and others. We care far more about your ability to adapt than whether you've used our exact stack before.
Bring experience with modern analytics platforms. Familiarity with tools like Mixpanel, Deepnote, AWS, Python, R, or application instrumentation is a plus, but not a requirement. Great analysts learn new tools. Great thinking is harder.

Responsibilities

How do we measure the success of AI-powered experiences? Help define the metrics and evaluation frameworks that tell us whether our agentic capabilities are delivering meaningful value to customers.
Which product changes actually create value for our customers? Design and analyze experiments that help Product teams separate signal from noise and make confident roadmap decisions.
How do we know if our products are healthy? Build the dashboards and reporting Product and Engineering leaders rely on to understand feature adoption, product performance, and where customers get stuck.
Which customer behaviors predict long-term success? Identify the signals that separate highly engaged customers from those at risk of dropping off, and help teams act on those insights.
Where are users struggling and what should we do about it? Analyze behavioral data, cohorts, and usage patterns to uncover friction points, identify opportunities, and help shape a better customer experience.
What should we build next? Partner with Product Managers and Engineers to answer high-impact business questions, evaluate tradeoffs, and use data to guide investment decisions.
What should we measure that we aren't measuring today? Partner with Product and Engineering to improve instrumentation, define meaningful metrics, and ensure we're capturing the data needed to make better decisions.
Create confidence in product decisions by designing, analyzing, and communicating experiments that help teams understand what works and what doesn't.
Turn product data into strategic direction by identifying trends, customer behaviors, and opportunities that influence roadmap priorities.
Build a shared understanding of product health through dashboards and reporting that give Product and Engineering teams visibility into adoption, performance, and customer outcomes.
Help define how we measure success in an AI-first product by developing the metrics and evaluation frameworks that shape our agent-powered capabilities.
Bring data into the room early. Partner with Product Managers, Engineers, and Designers to frame problems, evaluate tradeoffs, and make better decisions before roadmaps are finalized.
Reveal what different customers need by using cohort analysis, segmentation, and behavioral data to uncover meaningful patterns and opportunities for improvement.
Bring clarity to ambiguity. Start with questions – not predefined reports – and determine what needs to be measured, analyzed, or built to help teams move forward with confidence.