Lead Data Scientist

• Investigate revenue-generating opportunities and analyze the underlying data to size, scope, prioritize, and measure product changes.
• Design, build, deploy, and operate production ML systems, including batch inference, online services, and online learning models for personalization, experimentation, and automation.
• Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps teams to integrate ML systems into customer-facing features.
• Establish monitoring frameworks to evaluate the performance, reliability, and business impact of ML-powered experiences.
• Implement data, code, and model lineage practices that support secure, reproducible, compliant, and maintainable ML lifecycles.
• Collaborate with Data Engineering to improve the data ecosystem and pipelines that power experimentation and machine learning.
• Apply causal inference, experimentation, and advanced analytics to guide product and monetization decisions.
• Mentor other data scientists and help define best practices for advanced analytics, experimentation, and production ML development.
• Use Claude Code and other AI tools throughout data discovery, modeling, coding, testing, and experiment evaluation.
• Help establish and communicate effective AI-native workflows, raising the AI fluency and productivity of data science and engineering teams.
• Work closely with business leaders to translate analytical findings into actionable product and revenue strategies.

Requirements

• Advanced degree in a discipline requiring sophisticated statistical analysis, or equivalent professional experience.
• 6+ years of experience scoping, building, analyzing, and deploying ML-powered systems, including models shipped successfully to production.
• Significant programming experience with Python, scikit-learn, PySpark, and causal inference tooling.
• Strong understanding of software engineering practices, including testing, modularization, version control, and production-quality development.
• Professional experience applying modern causal inference and causal analysis techniques.
• Significant experience working with existing datasets, designing instrumentation to generate new data, and implementing transformations for complex analyses and ML development.
• Hands-on experience designing, monitoring, and analyzing experiments within consumer technology environments.
• Experience building ML systems or conducting experimentation within a B2C technology company, ideally involving subscriptions, transactions, advertising, or other revenue-generating products.
• Strong communication and project leadership skills, with the ability to influence cross-functional teams and communicate complex technical concepts clearly.
• A structured, hypothesis-driven approach to solving ambiguous problems using data and experimentation.
• Experience using LLMs or generative AI for advanced data processing, analysis, development, or research workflows.
• Experience with digital advertising and ad-tech infrastructure is highly desirable.
• Familiarity with ranking and grouping algorithms for content feeds is a plus.
• Experience contributing to or managing production feature stores and ML model registries is preferred.
• Experience partnering with product-line general managers, including exposure to revenue reporting and forecasting, is advantageous.
• Knowledge of subscription products, lifecycle marketing, user acquisition, geospatial data, or mobile location-based services is a plus.
• Strong ownership mindset and ability to critically review AI-generated code, analyses, and models while remaining accountable for everything shipped.
• Willingness to continuously experiment with emerging AI tools and turn useful discoveries into repeatable team practices.

Benefits

• Annual base salary of $175,000–$218,000 USD, with final compensation determined by experience, skills, background, and geographic location.
• Equity compensation through Restricted Stock Units (RSUs).
• Medical, dental, and vision insurance, with plans fully paid for U.S. employees.
• Life and disability insurance and additional supplemental benefit options.
• 401(k) plan with company matching.
• Paid parental leave.
• Mental Wellness Program and Employee Assistance Program (EAP).
• Flexible paid time off, companywide holidays, and summer and winter shutdowns.
• Learning and development programs to support ongoing career growth.
• Equipment, tools, and reimbursement support for an effective remote working environment.
• Remote-first work culture.
• Complimentary premium membership and connected-device benefits.
• An AI-native working environment where modern AI tools are integrated into data discovery, development, experimentation, and everyday productivity.
• An inclusive, mission-driven culture that values direct communication, integrity, customer impact, and high-impact execution.