Product Data Analyst - Fraud
Lemonade
Job Overview
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Job Description
What You're Applying For
We're looking for a Product Data Analyst - Fraud with fraud expertise to serve as Lemonade's fraud gatekeeper, helping us stay one step ahead of fraudulent claims.
You'll partner closely with our Analytics and other Data Teams, Special Investigation Unit, and Underwriting teams to flag and prevent potential risks that could impact our business. This role is about shaping fraud detection strategies by leveraging data to solve complex product and business problems - tackling analytical questions, monitoring key metrics, modeling intricate datasets, and delivering actionable insights that directly influence our strategy.
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
- Shape fraud detection strategies by analyzing claim patterns, testing new prevention methods, and building data-driven recommendations that protect the business
- Partner closely with cross-functional teams including Special Investigation Unit, Underwriting, Analytics, Product, and Data teams to flag risks and improve decision-making
- Design and execute A/B tests on key flows and processes to strengthen fraud defenses and optimize detection capabilities
- Monitor and analyze key metrics through fraud trend detection, feature tracking, experiments, and platform health monitoring to stay ahead of emerging threats
- Model complex datasets using Snowflake, Python, and other tools to uncover hidden fraud patterns and business risks
- Deliver actionable insights by presenting findings and recommendations to stakeholders across the company to drive product and business decisions
- Explore new data opportunities by leveraging internal and external data sources to improve fraud mitigation and push product strategy forward
What you'll need
- Adaptability, drive, and an efficiency mindset - we believe these matter most in human-AI collaboration
- 3+ years of data/product analysis experience in a fast-growing consumer company, with specific background in fraud detection and prevention
- Strong technical foundation with SQL expertise and experience using BI tools like Looker, Tableau, or Mixpanel
- A/B testing expertise and comfort working with large datasets to drive data-driven decision making
- Cross-functional collaboration skills - you'll work closely with product managers, data engineering, and data science teams
- Clear communication abilities in English, with experience presenting technical information to diverse stakeholders
- Educational degree in Industrial Engineering, Mathematics, Statistics, or equivalent experience
- Ready to work in an office environment most days a week
- Enthusiasm about learning and adapting to the exciting world of AI - a commitment to exploring this field is a fundamental part of our culture
Key skills/competency
- Fraud Detection
- Data Analysis
- SQL
- A/B Testing
- Risk Prevention
- Cross-functional Collaboration
- BI Tools (Looker, Tableau, Mixpanel)
- Data Modeling (Snowflake, Python)
- Stakeholder Communication
- AI/Machine Learning (adaptability)
How to Get Hired at Lemonade
- Research Lemonade's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Customize your resume to highlight fraud detection, data analysis, and A/B testing skills, matching the Product Data Analyst - Fraud role.
- Showcase AI enthusiasm: Emphasize your interest in AI and how you've leveraged data-driven insights in previous roles.
- Prepare for technical challenges: Be ready for SQL queries, data modeling discussions, and case studies relevant to fraud prevention and product analytics.
- Highlight collaboration skills: Provide examples of successful cross-functional teamwork, especially with product, engineering, and investigative units.
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