Lead Data Scientist

Brisk Teaching • San Francisco, CA

1d • Hybrid

About The Position

We've already started building something we think fundamentally rethinks how data science gets done at a company. We have an internal data agent that can query our warehouse, interpret results, and surface insights to stakeholders directly. It works. People use it. And it's changed how our team operates day to day.
Now we need someone to take it further. As Brisk's product and organization grow, the systems underneath need to grow with them: a more robust semantic layer, better tooling for experimentation, more sophisticated automation across the data lifecycle, and the strategic thinking to decide where AI amplifies the team's work and where it replaces manual processes entirely.
This role is equal parts strategy and execution. You'll own the vision for how data drives decisions across Brisk, scale the AI-native workflows we've started, and build the systems that turn a small data team into one that operates like a much larger org. You'll report directly to leadership and have a seat at the table on product strategy, go-to-market, and company priorities.

Requirements

• 8+ years of experience in data science, analytics engineering, or a related role. You've operated across the full analytics lifecycle and seen what works (and what doesn't) at scale.
• Comfort with ambiguity and speed: You've worked at a startup or similar environment where priorities shift fast, resources are limited, and scrappiness is a survival skill. You don't need a playbook to get started.
• Strategic instincts: You identify which questions matter, not just answer the ones you're given. You've partnered with product and business leaders to shape roadmaps, define success metrics, and make hard tradeoffs.
• Deep SQL and warehouse expertise: You think in SQL. You've built and maintained data models, designed schemas, and know what it takes to keep a warehouse clean, documented, and trustworthy.
• Pipeline and infrastructure experience: You've built data pipelines and dealt with data quality issues firsthand. You understand the plumbing as well as the dashboards.
• dbt experience: You've built and maintained production dbt models: transformations, testing, documentation. You understand how a well-structured dbt project makes everything downstream better.
• Strong product sense: You've worked closely with product teams, you understand what drives user behavior in software (ideally in edtech or SaaS), and you have good instincts for what to measure and what to build.
• Enough engineering chops to ship: You can build a working tool in Python, wrangle APIs, and write code that other people can maintain. You're not a SWE, but you're not afraid of production.
• Genuine excitement about AI: You've been building with LLMs. You have opinions about which models are good at what, you've experimented with agents or RAG systems, and you believe AI is going to fundamentally change how data teams operate.
• Builder mentality: You see a manual process and you can't help but automate it. You ship fast, iterate, and care more about impact than perfection.

Nice To Haves

• Experience with Snowflake.
• Background in edtech or K-12 education.
• Experience building internal tools (Slack bots, CLI tools, self-serve interfaces) that people actually used.
• A/B testing and experimentation design.
• Experience managing or mentoring data team members.
• Early-stage startup experience where you've built a data function from the ground up.

Responsibilities

• Set the data strategy: Define what we measure, how we measure it, and what "good" looks like. Establish the metrics architecture that connects product usage, retention, monetization, and growth into a coherent picture, and make sure every team at Brisk is oriented around it.
• Turn the data team into a product team: Build internal data products that stakeholders across the company actually use daily. Replace ad-hoc requests with self-serve AI interfaces, automated reports, and tools that make data accessible to non-technical teammates.
• Make the warehouse AI-readable: Build the semantic layer, documentation, and context infrastructure that lets both humans and AI systems query Brisk's data accurately. A well-documented warehouse is the foundation for every AI-powered workflow that follows.
• Build AI into the data lifecycle: Identify where AI agents, automated pipelines, and LLM-powered tooling can replace manual work: dbt model generation, data quality monitoring, experiment analysis, insight delivery. Ship these systems into production.
• Own product analytics and experimentation: Partner with Product, Engineering, and Design to design experiments, interpret results, and deliver the insights that shape what we build. Bring rigor to how we evaluate features and make ship/no-ship decisions.
• Drive growth and business intelligence: Maintain and evolve dashboards and reporting for Sales, Marketing, Customer Success, and leadership. Ensure the metrics that matter are visible, trusted, and actionable.
• Scale through systems, not headcount: Build infrastructure and AI-powered tooling that multiplies the team's output so a small data org can support a fast-growing company without scaling linearly.

Benefits

• Competitive salary
• Stock options, vested over 4 years
• Comprehensive benefits package, including health, dental, and vision insurance.
• Opportunities for professional growth and development.
• Collaborate with your teammates two times a week via our hybrid model in either our San Francisco or New York City offices.
• A supportive and collaborative work environment.
• The chance to make a meaningful impact on education through innovative technology.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed