Staff Data Scientist - Growth and Marketing

5d5 days ago

Charlie Health

New York City, US · Full-time · $190,000 – $270,000

About this role

Charlie Health connects the world to life-saving behavioral health treatment. We deliver personalized, virtual care rooted in connection for people with complex needs. We're expanding access to meaningful care from home and driving better outcomes.

As a Staff Data Scientist - Growth and Marketing, you'll act as a thought leader for Growth, Marketing, and Revenue partners. You'll inform roadmaps, shape thinking, and drive company growth through streamlined marketing measurement and statistical rigor. Leverage modeling and causal inference to identify new opportunities for impact.

Partner with Growth, Marketing, Revenue, and Operations to turn ambiguous questions into clear recommendations. Build lead scoring models, analyze high-performer patterns, and architect marketing measurement frameworks like MMM and incrementality testing. Apply causal inference and design experimentation frameworks for channels and audiences.

We're a rapidly growing team redefining behavioral health treatment. Join passionate professionals tackling the mental health crisis and increasing access to care. Use your skills to drive lasting change 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
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

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]), and align on consistent metric definitions (e.g. CAC, conversion, retention)
Develop spend optimization and 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 and guardrails for each approach
Design and scale repeatable experimentation frameworks for channel, creative, and audience testing, increasing velocity without compromising rigor