Machine Learning / Data Scientist - Member of Technical Staff at Harper

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.

• 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 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

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

• 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

• 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

• 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

• 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

• 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

• 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 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.

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