About the Role

The GSS Data Analytics team serves as Uber's analytics and AI solutions partner, helping Product, Operations, Engineering, and Data Science teams solve complex business challenges through scalable analytics, automation, and AI-powered solutions.

As a Program Manager II, you will lead high-impact initiatives from problem definition through execution, driving cross-functional alignment while delivering solutions that improve operational efficiency, decision-making, and business outcomes across Uber's global lines of business.

This role sits at the intersection of program management, advanced analytics, automation, and emerging AI technologies. You will partner with stakeholders across the company to identify opportunities, define success metrics, and build scalable solutions that unlock measurable business impact. - What You Will Do -

• Lead end-to-end analytics, automation, and AI initiatives by partnering with Product, Operations, Engineering, and Data Science teams to define business problems, establish success metrics, and drive execution from ideation through implementation.
• Utilize advanced analytical and visualization techniques to develop scalable end-to-end solutions, uncover actionable insights from large and complex datasets, and influence product, operational, and strategic decisions across Uber.
• Translate complex analytical findings and technical solutions into clear, concise, and compelling recommendations for both technical and non-technical stakeholders, enabling data-driven decision making at all levels of the organization.
• Drive multiple concurrent analytics, data engineering, and AI projects while navigating ambiguity, prioritizing effectively, and maintaining a high bar for quality, accuracy, and operational excellence.
• Design KPI frameworks, measurement strategies, and performance tracking systems that help teams monitor business health, evaluate initiatives, and identify opportunities for growth and efficiency improvements.
• Identify opportunities to leverage Machine Learning and Generative AI technologies, define solution strategies, translate business requirements into technical specifications, and partner with technical teams to deliver impactful AI-powered solutions.
• Build intelligent data products and automation solutions, including conversational assistants, recommendation systems, self-service tools, and workflow automations by leveraging Python, LLM APIs, and modern AI technologies.
• Lead vendor data analysts and project contributors, establish best practices and documentation standards, and continuously improve the scalability, efficiency, and quality of DAS team deliverables.
Cut through noise in large datasets to identify the core business questions, demonstrate strong attention to detail, and maintain a hands-on, scrappy approach to solving problems and unblocking teams when needed.

- What You Will Need -

• 6+ years of experience in Program Management, Analytics, Business Intelligence, Data Science, Data Engineering, or related quantitative fields.
• Advanced SQL and Python proficiency with experience developing analytical, automation, or AI-driven solutions using large-scale datasets.
• Experience leading and mentoring teams, including analysts, vendor resources, or cross-functional project teams, while driving high-quality execution and delivery.
• Strong analytical and problem-solving skills, with the ability to translate complex business challenges into scalable data, automation, or AI solutions.
• Experience communicating insights and recommendations to both technical and non-technical audiences and influencing decisions through data-driven storytelling.

- Preferred Qualifications -

• Experience applying Machine Learning, Generative AI, or advanced analytics techniques to solve business problems and drive measurable outcomes.
• Hands-on experience integrating LLMs and AI services through APIs to build intelligent applications, copilots, chatbots, or workflow automation solutions.
• Experience building or leading the development of AI-powered products such as recommendation systems, forecasting solutions, decision-support systems, or self-service analytics platforms.
• Strong understanding of ML development lifecycles, experimentation frameworks, model evaluation methodologies, and deployment considerations.
• Experience managing large-scale initiatives in fast-paced, ambiguous environments and driving alignment across multiple teams and organizations.
• Bachelor's or Master's degree in Statistics, Computer Science, Engineering, Economics, Mathematics, or a related quantitative discipline.