Product Analyst, AI & UX Optimization

Afficiency; New York (NY); On-site; USD 85,000 - 130,000; Full time

Job summary

Afficiency is hiring a hybrid analyst/builder to identify where users get stuck in product journeys and to prototype and ship fixes using AI tools like Claude.

You will review application flows, support tickets, and conversion data to drive measurable improvements, partnering with product, engineering, and underwriting to scope and deliver changes.

Responsibilities

• Own the ongoing review of Afficiency's product and UX/UI journeys — application flows, agent tools, and consumer-facing experiences — to identify friction, confusion, and drop-off points.
• Dig into real usage data: session recordings, support tickets, underwriting outcomes, and conversion funnels, to find where the product isn't working the way it should.
• Turn findings into concrete, prioritized recommendations grounded in business impact, not just "this feels off."
• Use AI tools such as Claude to prototype, build, or directly implement fixes — copy changes, flow adjustments, small tooling, automation, or reports — rather than only handing off findings.
• Partner with product, engineering, and underwriting stakeholders to scope and ship changes, escalating and collaborating when something needs deeper technical work.
• Build a repeatable habit of measuring whether a change actually worked, and iterating when it didn't.
• Bring plain business judgment to the table: does this make sense, would a real customer understand this, is this worth fixing now or later.

Job description

Afficiency builds the technology that powers modern life insurance — from the moment someone starts an application to the moment they get approved. Every product we ship has dozens of small UX and workflow decisions baked into it, and most of them were made under deadline pressure, not with the benefit of hindsight. We're hiring someone whose job is to go find those moments, figure out what's actually broken or clunky, and then use AI tools to go fix it — not just write it up and hand it off.

About Afficiency

Afficiency is an insurtech company building the digital infrastructure behind term life insurance — application journeys, underwriting automation, and the agent and consumer-facing tools that make buying life insurance faster and less painful. Our products span multiple carriers and channels, which means a lot of surface area, a lot of user journeys, and a lot of opportunity for a sharp, hands-on operator to make things measurably better.

About the Role

This is a hybrid analyst/builder role. You'll spend part of your time in our products and UX flows like a detective — reading session recordings, walking through application journeys yourself, digging into support tickets and drop-off points — to find where users get confused, stuck, or frustrated. Then, instead of stopping at a slide deck of recommendations, you'll use AI tools (Claude and others) to prototype and ship the fix yourself, or work directly with the team that can. A year in, success looks like a track record of real, shipped improvements you identified, scoped, and drove to completion — not a backlog of tickets someone else implemented.

What You'll Do

• Own the ongoing review of Afficiency's product and UX/UI journeys — application flows, agent tools, and consumer-facing experiences — to identify friction, confusion, and drop-off points.
• Dig into real usage data: session recordings, support tickets, underwriting outcomes, and conversion funnels, to find where the product isn't working the way it should.
• Turn findings into concrete, prioritized recommendations grounded in business impact, not just "this feels off."
• Use AI tools such as Claude to prototype, build, or directly implement fixes — copy changes, flow adjustments, small tooling, automation, or reports — rather than only handing off findings.
• Partner with product, engineering, and underwriting stakeholders to scope and ship changes, escalating and collaborating when something needs deeper technical work.
• Build a repeatable habit of measuring whether a change actually worked, and iterating when it didn't.
• Bring plain business judgment to the table: does this make sense, would a real customer understand this, is this worth fixing now or later.