Lead Data Scientist

<h3>Accountabilities</h3><li>Investigate revenue-generating opportunities and analyze the underlying data to size, scope, prioritize, and measure product changes.</li><li>Design, build, deploy, and operate production ML systems, including batch inference, online services, and online learning models for personalization, experimentation, and automation.</li><li>Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps teams to integrate ML systems into customer-facing features.</li><li>Establish monitoring frameworks to evaluate the performance, reliability, and business impact of ML-powered experiences.</li><li>Implement data, code, and model lineage practices that support secure, reproducible, compliant, and maintainable ML lifecycles.</li><li>Collaborate with Data Engineering to improve the data ecosystem and pipelines that power experimentation and machine learning.</li><li>Apply causal inference, experimentation, and advanced analytics to guide product and monetization decisions.</li><li>Mentor other data scientists and help define best practices for advanced analytics, experimentation, and production ML development.</li><li>Use Claude Code and other AI tools throughout data discovery, modeling, coding, testing, and experiment evaluation.</li><li>Help establish and communicate effective AI-native workflows, raising the AI fluency and productivity of data science and engineering teams.</li><li>Work closely with business leaders to translate analytical findings into actionable product and revenue strategies.</li><h2>Requirements</h2><li>Advanced degree in a discipline requiring sophisticated statistical analysis, or equivalent professional experience.</li><li>6+ years of experience scoping, building, analyzing, and deploying ML-powered systems, including models shipped successfully to production.</li><li>Significant programming experience with Python, scikit-learn, PySpark, and causal inference tooling.</li><li>Strong understanding of software engineering practices, including testing, modularization, version control, and production-quality development.</li><li>Professional experience applying modern causal inference and causal analysis techniques.</li><li>Significant experience working with existing datasets, designing instrumentation to generate new data, and implementing transformations for complex analyses and ML development.</li><li>Hands-on experience designing, monitoring, and analyzing experiments within consumer technology environments.</li><li>Experience building ML systems or conducting experimentation within a B2C technology company, ideally involving subscriptions, transactions, advertising, or other revenue-generating products.</li><li>Strong communication and project leadership skills, with the ability to influence cross-functional teams and communicate complex technical concepts clearly.</li><li>A structured, hypothesis-driven approach to solving ambiguous problems using data and experimentation.</li><li>Experience using LLMs or generative AI for advanced data processing, analysis, development, or research workflows.</li><li>Experience with digital advertising and ad-tech infrastructure is highly desirable.</li><li>Familiarity with ranking and grouping algorithms for content feeds is a plus.</li><li>Experience contributing to or managing production feature stores and ML model registries is preferred.</li><li>Experience partnering with product-line general managers, including exposure to revenue reporting and forecasting, is advantageous.</li><li>Knowledge of subscription products, lifecycle marketing, user acquisition, geospatial data, or mobile location-based services is a plus.</li><li>Strong ownership mindset and ability to critically review AI-generated code, analyses, and models while remaining accountable for everything shipped.</li><li>Willingness to continuously experiment with emerging AI tools and turn useful discoveries into repeatable team practices.</li><h2>Benefits</h2><li>Annual base salary of<strong>$175,000–$218,000 USD</strong>, with final compensation determined by experience, skills, background, and geographic location.</li><li>Equity compensation through Restricted Stock Units (RSUs).</li><li>Medical, dental, and vision insurance, with plans fully paid for U.S. employees.</li><li>Life and disability insurance and additional supplemental benefit options.</li><li>401(k) plan with company matching.</li><li>Paid parental leave.</li><li>Mental Wellness Program and Employee Assistance Program (EAP).</li><li>Flexible paid time off, companywide holidays, and summer and winter shutdowns.</li><li>Learning and development programs to support ongoing career growth.</li><li>Equipment, tools, and reimbursement support for an effective remote working environment.</li><li>Remote-first work culture.</li><li>Complimentary premium membership and connected-device benefits.</li><li>An AI-native working environment where modern AI tools are integrated into data discovery, development, experimentation, and everyday productivity.</li><li>An inclusive, mission-driven culture that values direct communication, integrity, customer impact, and high-impact execution.</li>