- **Manage the Talent Pipeline:** Oversee the rotation curriculum and technical onboarding systems that transform entry-level graduates into high-impact Analysts and Analytics Engineers. You will empower your Data Operations Leads to execute this curriculum daily. - **Strategic Recruitment:** Lead the selection and onboarding of regional Data Operations Leads and oversee the data-driven recruitment engine used to filter high-volume campus applications and secure top-tier talent. - **Define Technical Standards:** Establish the Sun King Data Standard, ensuring every graduate masters SQL, LookML, and the business logic required for specialized roles. - **Service Accountability:** Set the global strategy for the Data Desk. While your regional Leads manage daily ticket assignments, you are responsible for overall SLA performance, quality of output, and the health of escalation pathways. - **Operationalize Quality:** Partner with the Data Product team to define high-level SOPs. You will guide your Data Operations Leads in testing these procedures to ensure they are realistic for the junior workforce. - **Systemic Automation:** Lead the transition of manual tasks into automated self-service tools by working with the Engineering Hub. Your goal is to provide the Data Product team with high-quality assets that reduce the support burden on the desk. - **Management of Regional Leads:** Provide mentorship and professional development to the Data Operations Leads. You will ensure they are utilizing their squads effectively and maintaining technical rigor across all cohorts. - **Hands-On Mentorship:** While your focus is on the system, you will remain close to the technical work. You will not build executive dashboards daily, but you will oversee their development and get involved in troubleshooting and fixing code when pipelines or logic fail. **Empowerment over Centralization:** Success is measured by the speed at which you graduate talent and how effectively you enable the Data Product team to deploy new tools through the Data Ops infrastructure.

• Own analytical problems end to end from problem framing and metric definition through data acquisition, modeling, validation, and stakeholder rollout • Build predictive and inferential models such as forecasts, propensity and uplift models, and survival or duration analyses, where they meaningfully improve a product or operational decision • Develop statistical methods and reusable analyses for measuring performance, including detection accuracy, intervention impact, and downstream operational outcomes • Partner with Product, Engineering, and Operations to define key metrics, implement new features, and translate business questions into well-scoped analytical work • Audit system outputs and provide structured feedback that improves product accuracy and reliability • Communicate findings through dashboards, written analyses, and stakeholder presentations that lead to concrete decisions • Help define best practices around data quality, experimentation, modeling, and reproducibility, and contribute to the team’s data contracts and SOPs

• Owning end-to-end responsibility for building identity verification machine learning models from prototyping to production and and driving measurable impact in live environments • Taking full ownership for performance metrics like verification accuracy and false acceptance rates, and pushing the boundaries of what’s possible under high-security constraints • Influencing and shaping our in-house models, and evaluating external solution vendors, and improving instrumentation to generate valuable insights while creating new machine learning products • Establishing key metrics, deriving data-driven insights, and conducting experiments to assess the overall product impact of our biometric machine learning solutions on identity verification • Working both independently and collaboratively with other engineers to define and deliver high-impact product features in the biometrics domain

• Apply advanced AI/ML methods to solve business and customer problems • Design, develop, and scale machine learning models and prototypes suitable for production integration • Partner with engineering, product, and business teams within Agile delivery environments • Take end-to-end ownership of models, ensuring performance, reliability, and continuous enhancement across their lifecycle • Contribute to full-cycle development, including feature engineering and production readiness • Review code, provide peer feedback, and maintain high engineering and quality standards • Build and maintain strong working relationships with internal stakeholders and external partners • Convert complex analytical outputs into clear, actionable insights for both technical and non-technical audiences • Decompose ambiguous business challenges into well-defined data science problem statements • Stay up to date with industry advancements in AI/ML and actively apply new knowledge • Collaborate with other data science teams to strengthen standards, practices, and culture • Produce and maintain comprehensive documentation for models, workflows, and processes to support transparency and knowledge sharing