Product Data Analyst

We're looking for a Product Data Analyst to help us find the opportunities hiding in our data and turn them into real product improvements.

You'll be part of the Analytics team, embedded in product decisions from the research stage to final recommendation. You will be tackling analytical questions, monitoring the metrics that matter, and surfacing insights that actually change how we build.

Today, that means being genuinely fluent in AI — not just using it as a tool, but understanding it well enough to build with it, question it, and push it further.

We believe three things matter for every role at Lemonade: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.

Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.

In this role you’ll:

Collaborate with Analytics, Product, and Data teams to sharpen business decisions.

Initiate analyses and generate reports for feature tracking, experiments, and product health monitoring

Design A/B tests across our flows and processes to build better products

Analyze data using Snowflake, Python, and other platforms

Engage with AI agents as a core part of your work — whether that's building them, evaluating their behavior, or digging into the data to understand where and why they fall short

Present key findings to stakeholders across the company, turning data into clear, actionable direction

Map new data domains and identify signals that push our product forward

What you’ll need:

3+ years of experience as a data, product, or business analyst, ideally at a fast-growing consumer company

A degree in Industrial Engineering, Mathematics, Statistics, or a related field

Strong SQL skills and hands-on experience with BI tools (Looker, Tableau, Mixpanel, or similar)

Proven ability to work with and build AI-powered tools — you know your way around the technology, not just the outputs

Familiarity with A/B testing best practices and comfort working with complex datasets

The ability to work fluidly across product managers, data engineers, and data scientists, and to communicate technical findings clearly in English

Ready to work in an office environment most days of the week