Data Scientist - $81.73 - $96.15 per hour

About the Role

This opportunity is for a Staff Data Scientist focused on growth analytics, marketing analytics, product analytics, experimentation, causal inference, and full-funnel performance measurement. The role owns analytical strategy across acquisition, onboarding, engagement, reactivation, and first-appointment conversion.

This is a full-stack data science role that combines predictive modeling, experimentation, attribution, lifecycle analytics, analytics engineering, BI, semantic layer design, and executive-level communication. The position requires someone who can own the data foundation, build trusted self-service analytics, and translate complex analysis into clear business decisions.

What You’ll Do

• Own the analytical strategy for the end-to-end marketing funnel, from marketable lives to lead generation, omnichannel engagement, visit completion, and re-engagement.
• Analyze outbound and inbound funnel performance, including call center allocation, referral scheduling, lifecycle journeys, reactivation, and initial visit completion optimization.
• Own product analytics across onboarding, sign-up conversion, in-app engagement, and the member experience through the first completed appointment.
• Partner with product teams as the embedded analytical lead and help shape the product analytics roadmap.
• Serve as the executive-facing owner of marketing and product performance narratives, explaining changes in funnel conversion, marketable lives, and visit completion.
• Design and lead experimentation programs across marketing and product, including test design, causal inference, readout discipline, and intake processes for stakeholder-driven test ideas.
• Own experimentation platform implementation, instrumentation, configuration, and results interpretation.
• Own and improve attribution frameworks, including scheduling episode attribution, multi-touch attribution, and media mix modeling.
• Partner with growth marketing leadership to co-own the analytics roadmap and turn insights into action.
• Own and evolve dbt data models for marketing and product domains, from raw source modeling through mart-layer metrics.
• Ensure data quality, test coverage, documentation, and semantic layer design for reliable self-service analytics.
• Engineer context into BI and semantic layer environments so stakeholders and AI tools can reliably self-serve answers.
• Write clear metric definitions, model descriptions, and documentation that improve trust in analytics and AI-assisted querying.
• Translate findings into actionable recommendations for executive, marketing, product, clinical, and finance stakeholders.
• Raise the analytics team’s standards around experimentation design, dbt modeling patterns, context engineering, and analytical best practices.
• Operate as a technical leader by reviewing work, establishing best practices, and helping the team improve without direct people management.

Qualifications

• Bachelor’s degree, ideally in a quantitative or technical field such as Economics, Statistics, Computer Science, Operations Research, or Applied Mathematics.
• Master’s degree is a plus.
• 8+ years of experience in data science, analytics, or experimentation.
• Proven track record of driving measurable impact on growth, acquisition, lifecycle, or funnel outcomes.
• Deep hands-on expertise in experimentation and causal inference.
• Experience designing and interpreting rigorous tests, including A/B tests, quasi-experimental methods, and geo-lift tests.
• Ability to defend methodology choices and explain causal reasoning to business partners.
• Strong background in attribution modeling, including scheduling episode attribution, multi-touch attribution, and media mix modeling.
• Experience owning lifecycle analytics, ideally with tools such as Customer.io, Braze, Iterable, or similar platforms.
• Hands-on experience with product analytics instrumentation, including event tracking, funnel analysis, and experimentation platforms such as Statsig, Amplitude, Mixpanel, or equivalent tools.
• Experience with call center or contact center analytics is a plus.
• Expert-level SQL skills.
• Strong Python skills, including tools such as pandas, scikit-learn, and statsmodels.
• Production-level experience with dbt, including source modeling, mart-layer modeling, testing, documentation, and semantic layer design.
• Experience owning a dbt project end-to-end.
• Experience with context engineering for BI and AI self-service, including semantic layer definitions, metric descriptions, and data model documentation.
• Experience with BI or self-service analytics environments such as Omni, Looker, or equivalent tools.
• Fluency with AI-native developer and analyst tools such as Claude, Claude Code, Cursor, Hex AI Agent, Omni AI, or equivalent tools used in production analytical workflows.
• Experience with marketplace business models, healthcare, Medicaid, or similarly regulated domains is a plus but not required.
• Excellent communication skills with the ability to turn complex models, test results, and funnel diagnostics into clear recommendations for executive and marketing leadership audiences.
• Ability to work with minimal guidance, take ownership, and serve as the single point of accountability for critical analytical domains.

Benefits

• Remote-first work environment
• Flexible remote location
• Unlimited paid time off
• Medical, dental, and vision coverage
• 401(k)
• Bonus eligibility
• Registered dietitian sessions