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), analytical (OLAP), and event-based systems.
• Work across SQL (e.g., Postgres, Snowflake) and NoSQL (e.g., Redis, document/Key-Value stores).
• Design data assets and features that are usable by both analytics workflows and LLM/ML systems.

Data quality, measurement, and monitoring

• Define and track data quality metrics (completeness, consistency, timeliness, drift, schema changes).
• Build checks, monitors, and alerts to detect data issues that can affect analytics or AI/LLM performance.
• Work with data and engineering teams to diagnose root causes and drive changes that improve data quality over time.

Applied ML fundamentals

• Use core ML concepts (feature design, model evaluation, bias/variance, generalization) to reason about LLM and non-LLM approaches.
• When appropriate, build and evaluate lighter-weight or traditional models (e.g., scoring, ranking, classification) to complement or replace LLM solutions.

About You:

• A track record of high ownership: taking responsibility for problems end-to-end, improving systems rather than just describing them, and pushing initiatives across product, engineering, and data.
• A genuine love for messy, real-world data, and the curiosity to dig into anomalies until you understand what's really happening.
• Hands-on experience with real-world transactional data in production environments, including messy, incomplete, or biased data.
• Demonstrated experience improving data quality in production environments.
• Demonstrated experience shipping LLM-based product features, such as:
• Using hosted LLM APIs or in-house models
  • Designing prompts and workflows
  • Evaluating and iterating on LLM behavior using real user data
• Experience in product analytics, including:
• Defining and tracking product KPIs and feature-level metrics
  • Building and interpreting funnels, cohorts, and retention/engagement analyses
  • Influencing product decisions and roadmaps with data-driven insights
• Experience measuring and improving data quality, and working with engineering to fix upstream issues.
• Strong communication skills: ability to work cross-functionally and explain technical decisions and trade-offs to non-technical partners.
• Strong SQL skills: complex joins, window functions, CTEs, and performance-aware querying.
• Solid Python skills for data and AI work (e.g., pandas, NumPy, scikit-learn; OpenAI, Anthropic, and Gemini LLM libraries/frameworks).
• Formal education in machine learning concepts, such as:
• Supervised/unsupervised learning
  • Model selection and regularization
  • Evaluation methodologies (train/validation/test splits, cross-validation, experiment design)

Nice To Have:

• Experience with LLM tooling and patterns (e.g., RAG, vector databases, prompt/tool orchestration frameworks).
• Familiarity with experimentation platforms and A/B testing frameworks.
• Exposure to MLOps / LLMOps: model versioning, monitoring model & LLM feature performance, feedback loops.
• Experience with cloud data platforms (AWS, GCP, or Azure) and tools like Snowflake, dbt, or Dagster.

What this role is not

• A pure research role focused on publishing papers or building models in isolation from product.
• A heavy production engineering role requiring expert-level distributed systems skills.
• A pure BI role limited to reporting, with no involvement in AI/LLM feature development.

If you’re excited about applying LLMs and data to build real product features – and you enjoy owning the analytics and iteration loop around them – we’d love to hear from you.

Vouch provides several benefits to help you bring your best self to work:

• 💰 Competitive compensation and equity packages
• ⚕️ Health, dental, and vision insurance
• 🍼 Parental leave
• 🌴 Flexible vacation time
• 🪷 Wellness allowance
• 🛜 Technology allowance
• 📚 Company-sponsored personal and professional development
• 🏫 L&D: Partnerships with Ethena and monthly Lunch & Learns
• 🧘 Wellbeing: access to many wellbeing perks, including  Peloton, Fetch, OneMedical, Headspace care+, etc.
• 🤗 Caregiver Support: company seed into the dependent care FSA and company sponsored Care.com membership.
• 📊 Regular performance reviews: Vouch conducts regular performance discussions with all team members, offering goal setting and check-ins, development discussions, and promotion opportunities.

What to expect in a typical interview process:
(Please note these steps may vary slightly depending on the role)

• 30-minute phone call with our recruiting team
• 30-45 minute video interview with the hiring manager
• Case study/technical screen
• Meet the team! 30-45 min 1:1 video discussion with 3-4 team members you’d work closely with in the role
• Executive chat

Compensation philosophy:

Our salary ranges are based on paying competitively for our size and industry and are part of our total compensation package, which also includes benefits and other perks. We also include stock options in all compensation packages and believe all Vouch employees should have the opportunity to become owners in the company. Individual pay decisions are based on a number of factors, including qualifications for the role, experience level, skill set, location, and business need. The pay range provided is subject to change and may be modified in the future.

Vouch believes in putting our people first, and building a diverse team is at the front of everything we do. We welcome people from different backgrounds, experiences, perspectives, and ranges of abilities. We are an equal-opportunity employer and celebrate the diversity of our growing team.