Staff Data Scientist, Growth

About the role

Alpaca is a US-headquartered, global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. Amongst our subsidiaries, Alpaca is a licensed financial services company, serving hundreds of financial institutions across 40 countries with our institutional-grade APIs. This includes broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, totalling over 10 million brokerage accounts. Our global team is a diverse group of experienced engineers, traders, and brokerage professionals who are working to achieve our mission of opening financial services to everyone on the planet. We're deeply committed to open-source contributions and fostering a vibrant community, continuously enhancing our award-winning, developer-friendly API and the robust infrastructure behind it. Alpaca is proudly backed by $400 million in funding from top-tier global investors including Portage Ventures, Spark Capital, Tribe Capital, Social Leverage, Horizons Ventures, Opera Tech Ventures, SBI Group, Derayah Financial, Unbound, Peak XV, Elefund, and Y Combinator. Our Team Members: We're a dynamic team of 400+ globally distributed members who thrive working from our favorite places around the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, the UK, and beyond! We're searching for passionate individuals eager to contribute to Alpaca's rapid growth. If you align with our core values—Stay Curious, Have Empathy, and Be Accountable—and are ready to make a significant impact, we encourage you to apply.

Responsibilities

• Own the growth funnel end-to-end. Analyze acquisition, activation, retention, referral, and monetization across the full user journey; find and size the highest-leverage opportunities to grow the business.
• Run the growth experimentation engine. Design and execute A/B tests and causal/incrementality studies on onboarding, activation nudges, funding conversion, pricing, lifecycle/CRM campaigns, and referral loops—building the guardrails, metrics, and self-serve tooling that let teams experiment with rigor and velocity.
• Model growth. Build retention/cohort analyses, churn and LTV models, CAC/payback and unit-economics views, and forecasts of core growth metrics; identify the compounding levers and growth loops worth investing in.
• Measure what marketing and partnerships actually drive. Use attribution, incrementality testing, and channel/marketing-mix analysis to guide acquisition spend, CAC, and payback.
• Define the growth metrics that matter. Define and maintain activation, retention, engagement, and monetization metrics and a clear North Star; partner with analytics engineering to build governed, scalable metrics and dashboards teams rely on daily.
• Embed cross-functionally. Partner closely with Product, Marketing, Sales, Engineering, and Finance to shape strategy