SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Berry Street is a Series B healthtech company building AI-powered metabolic care and chronic disease management, leveraging GLP-1s and insurance to make treatment accessible across all 50 states. The platform connects patients with 2,000+ clinicians and AI tools, serving hundreds of thousands of patients and backed by Khosla, Northzone, and founders from Revolut, Grow Therapy, and Spring Health.
You will be Berry Street's first Growth Data Scientist, owning how the company measures marketing impact and decides where to invest growth capital. Reporting to the Head of Data and partnering closely with the Head of Growth, you'll work across Growth, Product, and Engineering to drive patient acquisition while improving LTV and CAC metrics.
Key responsibilities include:
• Establish measurement standards for marketing impact using experiments, incrementality testing, attribution modeling, and MMM (media mix modeling) as appropriate.
• Build decision frameworks using causal inference, Bayesian methods, regression, and forecasting to guide budget allocation, channel mix, and creative strategy toward better patient acquisition economics.
• Ensure fast decisions remain trustworthy by accounting for the lag between ad events, bookings, visits, and retained patients—validating short-term signals against long-term outcomes.
• Productionize your best analytical work into automated decision systems and models that the team relies on daily, including in-house ad optimization processes.
• Identify growth levers beyond marketing (booking flow, provider availability, ad platform signals) and drive cross-functional changes.
You'll work in a fast-moving environment where ad platforms change daily and priorities shift, but where your analytical rigor and decision frameworks directly shape company spending and patient outcomes.
REQUIREMENTS:
• 5–8+ years in data science or marketing science at a company with meaningful paid acquisition, with a track record of owning analytical work end-to-end and changing how money gets spent.
• Strong foundation in statistics: experimental design, causal inference, and predictive modeling.
• Deep understanding of marketing measurement (multi-touch attribution, MMM, incrementality testing) and when each method applies.
• Fluency in SQL and Python; ability to build datasets from messy ad platform, product, and warehouse data.
• Technical rigor: selecting the right method for each question, knowing when evidence is sufficient to act, and building statistically sound, reproducible, robust solutions.
• Ability to communicate clear, persuasive recommendations to technical and non-technical audiences that drive adoption.
• Bonus: Experience with privacy-constrained measurement (HIPAA, server-side or consented tracking), value-based bidding, or predicted-LTV signals. Familiarity with BigQuery, dbt, RudderStack, or Freshpaint.
