Applied Data Scientist

About Vouch:

Vouch is the insurance broker that powers ambition.

We’re a tech-enabled insurance advisory and brokerage purpose-built for growing companies in technology, life sciences, and professional services. Our clients are ambitious leaders building complex businesses, and we help them manage risk with tailored advice, smart coverage, and responsive service.

Backed by over $200M from world-class investors, Vouch combines deep industry expertise with AI-powered tools to deliver a better insurance experience. Our digital workflows reduce friction, speed up decisions, and give our clients the confidence to move faster.

Why should you join our team and Vouch?

Not only is this an exciting and growing team where you can drive a real impact on our operational scalability, but Vouch is also the preferred insurance provider to customers of Y Combinator, Brex, Carta, and WeWork. We’re a quickly growing startup that believes in transparency and acknowledgment with our team members and cultivating a values-driven company. Our values are "Be Client Obsessed", "Own it together", "Act with integrity and empathy", "Stay Curious and Grow", and "Empower People."

What does a work environment look like at Vouch?

Vouch has employees located across the U.S., with offices in San Francisco, Chicago, and New York City. This role can be based anywhere in the U.S. as long as you can work our Vouch core collaboration hours (8:30 am-2:30 pm Pacific Time) when most internal meetings are held.

About the role:

 
We’re looking for an Applied Data Scientist who is excited about using data and modern AI – especially large language models (LLMs) – to build and iterate on product features.

We’re looking for someone who genuinely enjoys working with messy, imperfect, real-world data – the kind that never quite fits the schema, arrives late, has surprises hidden inside it, and reflects actual user behavior. You should find energy in tracking down anomalies, debugging unexpected patterns, and getting to the root cause of data issues that affect product decisions and AI features.

This role also requires a high-ownership mindset: you don’t just answer questions – you help define which behaviors matter. You proactively identify data quality issues, measurement gaps, and opportunities for product improvement, and you drive these changes across the organization with persistence and clarity.

You’ll work with real-world transactional data (both SQL and NoSQL), design and ship LLM-powered experiences, and own the product analytics that measure their impact. You’ll help define what to build, how to measure it, and what to do next based on the data. This is neither a research scientist nor software engineering role – strong SQL, Python, proof-of-concept development, and product analytics skills as well as experience working with production data systems are what matter most.

What You’ll Do:

Build and iterate on LLM & AI-powered product features

• Design, prototype, and ship features that use LLMs (e.g., content generation, summarization, classification, semantic search, assistants, recommendations).
• Work with engineers to integrate LLMs into the product via APIs or internal services (RAG, tools/functions calling, workflows, pipelines).
• Define evaluation strategies for LLM features (e.g., human-in-the-loop evaluation, rubrics, prompt experiments, offline/online metrics).
• Continuously refine prompts, data pipelines, and system design based on user behavior, quality metrics, and product goals.

Own product analytics for data- & AI-powered features

• Partner with product managers and designers to define success metrics (e.g., adoption, engagement, conversion, retention, quality, time-to-value).
• Instrument new features: define events, ensure proper logging, and validate that data is correct and trustworthy.
• Analyze funnels, cohorts, user journeys, and experiment results to understand drivers of behavior and outcomes.
• Translate insights into clear recommendations that influence roadmaps, prioritization, and feature iteration.

Work with real-world transactional data (SQL & NoSQL)

• Explore, clean, and transform data from transactional (OLTP), ana