Senior Data Scientist, Growth

Berry Street is building on the convergence of AI, GLP-1s, and the consumerization of healthcare — while leveraging insurance to make life-changing metabolic care as accessible as possible for all. Our platform connects patients with 2,000+ clinicians and AI tools to treat obesity, diabetes/prediabetes, cardiovascular disease, kidney disease, maternal health, and dozens of other chronic and acute conditions, working with major health plans, health systems, and care management companies across all 50 states.

We launched in 2023, have served hundreds of thousands of patients, and are Series B — backed by investors like Khosla, Northzone, FJ Labs, and the founders of Revolut, Grow Therapy, and Spring Health.

About the Role

Berry Street grows through paid social, search, display, partnerships, affiliate, content, and lifecycle marketing, and we invest meaningfully in all of them. As we scale, the questions get harder: which of those investments actually create new patients, where the next dollar should go, and how to make every patient we acquire worth more. Ad platform reporting only tells part of the story.

We're hiring our first Growth Data Scientist to own how Berry Street measures growth and decides where to invest in it. Your mission is to grow patient volume while improving LTV and CAC, and to build the measurement and models the company relies on to do that every day, not one-off analyses. You'll report to our Head of Data, partner closely with our Head of Growth, and work across Growth, Product, and Engineering.

This is a role for a decider and a builder: you bring a strong point of view, push it until it changes what we do, and help make the change yourself.

What You'll Do

• Own how we measure growth. Set the standard for how Berry Street measures marketing impact, using experiments, incrementality testing, attribution, and MMM, each where it fits.
• Decide where the next dollar goes. Use the full toolbox (causal inference, Bayesian methods, regression, forecasting) to steer budget, channel mix, and creative toward more patients at better LTV/CAC, and turn open-ended budget questions into clear decision frameworks the company acts on.
• Make fast decisions trustworthy. Ads change daily and bookings come in fast, but a booking isn't a visit, and a visit isn't a patient who comes back. Make sure the calls we make in days hold up against the outcomes that take months to show.
• Build things that last. Turn your best work into production models and automated decision systems, including our in-house ad optimization process, that the team relies on every day.
• Look beyond your lane. The biggest growth lever isn't always marketing. It might be the booking flow, provider availability in a state, or the signal we send to ad platforms. Find it, make the case, and drive the change with whoever needs to be involved.

Requirements

• 5–8+ years in data science or marketing science at a company with meaningful paid acquisition, and a track record of owning analytical work end-to-end and changing how money gets spent.
• A strong foundation in statistics: experimental design, causal inference, and predictive modeling.
• A deep understanding of marketing measurement, not just channel mechanics. You know what a multi-touch model can and can't tell you, when MMM is worth the investment, and how to check both against incrementality reads.
• Fluency in SQL and Python, and comfort building your own datasets from messy ad platform, product, and warehouse data when the measure you need doesn't exist yet.
• Technical rigor that holds up. You pick the right method for the question, know when good-enough evidence is enough to act, and build solutions that are statistically sound, reproducible, and robust to messy data.
• The ability to sell a decision: clear, persuasive recommendations for technical and non-technical audiences that actually get adopted.
• Bonus: Measurement in a privacy-constrained setting (HIPAA, server-side or consented tracking), or experience with value-based bidding and predicted-LTV signals. Our stack includes BigQuery, dbt, RudderStack, and Freshpaint.
• Who You Are
• Proactive. You don't wait to be asked. You spot problems and opportunities before anyone else does.
• At home in ambiguity. Priorities here shift fast. You make progress anyway, and you'd rather shape the agenda than wait to be handed one.
• A systems thinker. You put effort where it compounds, and you build reliable, resilient systems that keep working as requirements change.
• AI-native. Agentic data exploration and LLM-assisted analysis are part of how you work every day. You're open to automating whatever should be automated, and you know where the judgment still has to come from you.