Staff Data Scientist (Growth & Marketing)

Charlie Health•New York, NY

17h•Hybrid

About The Position

Charlie Health is a rapidly growing organization dedicated to connecting people with life-saving behavioral health treatment. We deliver personalized, virtual care focused on connection and improving outcomes for individuals with complex needs. As a Staff Data Scientist - Growth and Marketing, you will be a thought leader, partnering with Growth, Marketing, and Revenue teams to leverage data for strategic decision-making. You will contribute directly to company growth by refining marketing measurement, applying statistical rigor through modeling and causal inference, and identifying new opportunities for sustained impact across the organization. This role is for passionate, forward-thinking professionals inspired by our mission to increase access to mental healthcare and impact millions of lives.

Requirements

7+ years in data science/analytics roles, at least 3 of those years with Marketing, Sales, and/or Revenue teams
Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders
Familiarity with CRMs (e.g. Salesforce), Marketing Platforms, and the metrics / terminology associated with them (CAC, Opps, etc.)
Experience with modeling work that spans at least some of: MMM, LTV, Lead Scoring, Attribution Modeling, and Segmentation
Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement)
Excellent ability to communicate, collaborate, and influence business partners at all levels

Nice To Haves

Healthcare Experience
Start-up Experience
Passion for mission-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes

Responsibilities

Partner with Growth, Marketing, Revenue, and Operations to turn ambiguous business questions into clear analytical recommendations that influence strategy and decision-making
Build and iterate on lead, account, and ICP scoring models that help reps prioritize efforts and partner with Ops on data-driven territory design
Develop models to analyze successful high-performer rep patterns to optimize onboarding and productivity
Architect and maintain a trusted marketing measurement framework (e.g. MMM, incrementality testing [geo tests, holdouts, lift studies]), & align on consistent metric definitions (e.g. CAC, conversion, retention)
Develop spend optimization & channel allocation frameworks that account for saturation, diminishing returns, and the tradeoff between short-term acquisition and long-term outcomes
Apply causal inference methods (DiD, synthetic controls, quasi-experiments) to own channel-level incrementality measurement, with clear decision rules & guardrails for each approach
Design and scale repeatable experimentation frameworks for channel, creative, and audience testing, increasing velocity without compromising rigor