Data Scientist

• Own end-to-end quality and delivery for critical Human Data projects while ensuring accuracy, consistency, and guideline adherence at scale.
• Lead, coach, and grow a team of Team Leads and AI Tutors (full-time and contractors), including performance management, career development, and talent pipeline building.
• Manage a globally distributed workforce spanning multiple employment types (full-time employees and contractors), fostering strong collaboration and consistent standards across time zones.
• Collaborate closely with Members of Program Staff, Go-to-Market teams, and Engineering teams to understand model needs and translate them into clear data requirements and labeling strategies.
• Define and continuously improve operational standards, annotation guidelines, taxonomy, and quality assurance processes.
• Drive efficiency and scalability by identifying and resolving bottlenecks, implementing process improvements, and leveraging metrics/KPIs to track performance (e.g., send-back rate, tutor utilization, lead time).
• Recruit, hire, and develop top talent while maintaining a high-performance, meritocratic culture with strong accountability.
• Build and iterate training programs, certification processes, and onboarding systems for new team members and leaders.
• Act as the voice of the tutors while connecting Team Leads with Human Data Engineers to drive continuous improvement.
• Report on program status, risks, and outcomes to senior leadership while fostering cross-functional alignment.

Manager, Human Data Operations

• Design and improve graph-based approaches for identity resolution across devices, households, and identifiers which improve match quality, coverage, and stability across a rapidly changing identity landscape.
• Use Scala, Spark, SQL, and cloud-native tools to analyze large identity datasets, build models, and productionize data science workflows.
• Contribute production-grade code to shared repositories, using strong engineering practices to build clear, scalable, and maintainable systems.
• Define validation strategies and measure model performance and business impact on targeting, measurement, and attribution.
• Help shape the team’s approach to identity science and partner across Engineering, Product, and Analytics to deliver production-ready solutions.
• Leverage LLMs, AI editors (Cursor, Copilot, Claude Code), and agentic workflows to accelerate research, prototyping, documentation, testing, and iteration.
• Apply privacy-by-design principles to ensure identity science work is auditable, compliant, and aligned with governance standards.

Senior Data Scientist

• Partner with senior scientists to forecast cash flows and other drivers of business health using statistical and ML modeling.
• Design and build models that make our capital products better for customers and deliver positive return assets. Sharper pricing, lower losses, growth and offers that fit how SMBs actually run.
• Proactively mine our datasets for insight, and prototype new models that move the needle for customers and the business.
• Own model outcomes end-to-end. Not just deployment bespoke to the platforms Pipe serves (e.g., UberEats and Housecall Pro), but ongoing performance, residual analysis, drift detection, and the judgment calls about when a model needs to be retrained, replaced, or retired. You'll work closely with product, risk peers and engineering to make this real in production.

• Independently developing and owning statistical models that forecast cash flows and other drivers of business and customer health.
• Designing and building models that optimize our product offerings to better serve our customers' needs.
• Proactively exploring datasets to surface insights, and prototyping new approaches that create measurable value for customers.
• Taking full outcome ownership of deployed models, including performance monitoring, residual analysis, and iteration, in close collaboration with engineering.
• Mentoring junior team members, raising the technical bar through code and design review, and contributing to our data science best practices and long-term tooling roadmap.
• Partnering with engineering and data engineering to productionize models, shape data infrastructure requirements, and improve our MLOps foundations.

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Manager, Human Data Operations

Senior Data Scientist

Data Scientist

Senior Data Scientist