The Mission

We're building an AI-powered insurance brokerage that's transforming the $900 billion commercial insurance market by automating processes that currently run on pre-internet systems. Fresh off our $8M seed round, we're looking for an exceptional Data Scientist who can turn our growing data assets into actionable insights that drive business growth and decision-making.

You'll work alongside our growth and sales teams to identify opportunities, optimize funnels, and create metrics that define how our business is growing. Working closely with our AI Context Engineers, you'll leverage the data streams and infrastructure they build to generate high-quality analytics and datasets for analysis. Your insights will be essential in driving top-of-funnel growth and other critical metrics as we scale.

We're committed to building a data-driven organization where decisions are backed by evidence and insights. You will work directly with the CEO to develop key metrics, forecasting recommendations, and address evolving business requirements. We live by core principles: "There is no try, there is just do," "Actions lead to information, always default to action," and "Strong opinions lead to information." We need data scientists who can quickly turn insights into impact, not just create interesting analyses.

Outcomes You'll Drive

Define and track key business metrics that accurately measure growth and performance
Develop prediction and recommendation models for forecasting business outcomes
Build and maintain dashboards and reports using Metabase, Turntable, and other tools
Identify patterns and trends in customer acquisition, conversion, and retention
Provide data-driven recommendations to optimize marketing spend and sales efforts
Partner with AI Context Engineers to ensure data pipelines meet analytical needs
Reconcile data from multiple sources to create consistent, reliable datasets
Communicate insights effectively to stakeholders across the organization
Develop and validate hypotheses about our business through rigorous analysis
Help architect our evolving data systems to support analytics and business intelligence

You're Our Person If

You have a strong analytical mindset and can translate complex data into actionable insights
You've worked in a fast-paced startup environment, ideally on a COO's team or founding team
You have experience at a data-driven organization where metrics drive decision-making
You can work with data in different modalities and formats
You're comfortable developing prediction and recommendation models
You can balance technical rigor with pragmatic solutions that deliver business value
You have a knack for identifying the right metrics that truly matter for business growth
You communicate complex ideas clearly to both technical and non-technical audiences
You're willing to get your hands dirty with data cleaning and pipeline issues
You ship analyses quickly and take immediate action instead of overthinking
You embrace "there is no try, there is just do" as your working philosophy

Hard Requirements

Degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, or related)
3+ years of experience in data science, analytics, or similar roles
Strong programming skills in Python and SQL
Experience with data visualization tools like Metabase, Tableau, Looker, or similar
Proficiency with statistical analysis and machine learning techniques
Ability to work with incomplete and imperfect data in a startup environment
Experience communicating analytical findings to business stakeholders
Background in growth metrics, funnel optimization, or revenue analytics
Knowledge of A/B testing and experimental design
Self-directed problem-solving approach
Must be based in San Francisco and work in-office 5.5 days per week (relocation assistance provided)

Our Tech Stack

We're building a modern, data-driven organization with these tools:

Analytics & Data Stack:

Metabase and Turntable for visualization and business intelligence
Python data science ecosystem (pandas, numpy, scikit-learn, etc.)
SQL for data extraction and analysis
ClickHouse for high-performance analytics queries
PostHog for product analytics and event tracking
Potential integrations with modern ML platforms as needs evolve

Data Infrastructure (that you'll work with):

Event sourcing architecture maintained by our AI Context Engineers
Vector databases for semantic search and AI applications
Apache Airflow, Temporal, Airbyte for data pipeline orchestration
Redis streams and PostgreSQL for operational data storage

What You'll Build in Your First 90 Days

First Month:

Develop deep understanding of our current data sources, pipelines, and limitations
Build initial dashboards tracking key business metrics for growth and sales teams
Establish baseline analytics for customer acquisition, conversion, and retention
Partner with the CEO to define the core metrics that will drive the business
Audit existing data quality and identify gaps or inconsistencies

Second Month:

Create predictive models for customer conversion and LTV
Develop recommendation frameworks for optimizing marketing spend
Build more sophisticated funnel analysis tools and visualizations
Work with AI Context Engineers to refine data pipelines for improved analytics
Implement A/B testing framework for growth experiments

Third Month:

Build forecasting models for business planning and resource allocation
Develop automated anomaly detection for key business metrics
Create cohort analysis tools to track customer behavior over time
Implement attribution modeling to understand marketing effectiveness
Design a data quality monitoring system to ensure reliable analytics

Our Data Philosophy

Metrics that Matter: Focus on the few key metrics that truly drive business growth and decisions
Insights to Action: Analysis is only valuable when it leads to concrete actions and improvements
Speed Over Perfection: Deliver fast, actionable insights rather than perfect but delayed analysis
Full-Funnel Visibility: Understand and optimize every stage of the customer journey
Data-Informed Culture: Foster an organization that bases decisions on evidence, not opinions
Cross-Functional Collaboration: Work closely with all teams to understand their data needs
Action Orientation: Always default to action - analyze, recommend, and iterate rather than overthink
Execution Focus: There is no try, there is just do - we value data scientists who drive outcomes
Strong Opinions: Form and express clear viewpoints backed by data that can guide decision-making
Continuous Improvement: Constantly refine our data systems, models, and analytical approaches

Join Us To Transform the $900B Insurance Market

This is an early-stage role at a fast-moving startup, and you'll often experience the crawl-walk-run approach to building. You'll quickly develop analyses and insights, then refine them into robust, scalable analytics systems. We're looking for people who can be creative in providing impact first, then take learnings from that impact and push them back into the system.

You should ideally have worked in an early-stage startup environment and understand the pacing. This is a fast-paced environment where we value ownership and quick, rapid feedback loops within the team. You'll work directly with the CEO, growth team, sales team, and AI Context Engineers to execute on our vision with a bias toward action.

We require you to be in San Francisco and work from our office 5.5 days per week. We'll cover relocation costs and believe the best teams collaborate intensively in person.

Skills

Python, SQL, Data Analysis, Statistical Modeling, Machine Learning, A/B Testing, Data Visualization, Metabase, Turntable, Growth Analytics, Funnel Optimization, Cohort Analysis, Forecasting, Business Intelligence, Experimental Design, Data Communication

Harper

Data Scientist - Member of Technical Staff

About the role