Senior/Staff Data Scientist, Infrastructure

About Glean:

Founded in 2019, Glean is an innovative AI-powered knowledge management platform designed to help organizations quickly find, organize, and share information across their teams. By integrating seamlessly with tools like Google Drive, Slack, and Microsoft Teams, Glean ensures employees can access the right knowledge at the right time, boosting productivity and collaboration. The company’s cutting-edge AI technology simplifies knowledge discovery, making it faster and more efficient for teams to leverage their collective intelligence.

Glean was born from Founder & CEO Arvind Jain’s deep understanding of the challenges employees face in finding and understanding information at work. Seeing firsthand how fragmented knowledge and sprawling SaaS tools made it difficult to stay productive, he set out to build a better way - an AI-powered enterprise search platform that helps people quickly and intuitively access the information they need. Since then, Glean has evolved into the leading Work AI platform, combining enterprise-grade search, an AI assistant, and powerful application- and agent-building capabilities to fundamentally redefine how employees work.

• Become a deep expert in Glean’s product telemetry, billing data, and platform systems, partnering closely with Product, Finance, and Infrastructure to self-serve insights
• Develop and evolve metrics that capture enterprise readiness, deployment health, and time-to-value, from initial rollout to sustained adoption
• Drive deployment and infrastructure cost optimization by analyzing usage patterns, workload drivers, and scaling behavior across customers.
• Partner with platform and infrastructure teams to model and optimize the economics of core systems such as connectors, indexing, and knowledge graph infrastructure, ensuring margins scale sustainably with usage growth
• Support pricing, packaging, and monetization decisions through thoughtful analysis of usage, consumption, and cost drivers
• Improve observability and data quality for billing and enterprise-critical workflows, identifying gaps and driving alignment across systems
• Build analytics and models to support security and trust initiatives, including threat detection, abuse patterns, and monitoring for enterprise-critical products 
• Analyze and improve the cost efficiency of compute-intensive workflows (eg: mining and indexing), identifying trade-offs between product value, performance, and unit economics.
• Lead cross-functional data science projects end-to-end, translating ambiguous problems into clear, actionable insights
• You will collaborate closely with Product Management, Engineering, Finance, Billing Operations, and GTM leadership to ensure Glean’s pricing, billing, and enterprise readiness strategies are grounded in high-quality data and clear, trusted metrics.

• 7+ years of experience in a highly quantitative data science role, with a degree in Statistics, Mathematics, Computer Science, or a related field
• Strong proficiency in SQL and Python, and experience working with modern data stacks (e.g., dbt, analytics engineering pipelines)
• Experience analyzing nascent, complex datasets and translating them into clear, actionable insights
• A strong product and business mindset, with experience defining KPIs, guardrail metrics, and dashboards that influence decisions
• Solid grounding in statistics, including experimentation and non-experimental methods
• Ability to independently own projects end-to-end, from problem framing to delivery
• Clear, concise communication skills, with the ability to explain complex analyses to both technical and non-technical audience
• You have experience in B2B SaaS, especially in the enterprise AI space.
• You have a very strong sense of ownership and self-motivation. You are laser-focused on delivering business impact while growing as an individual along with Glean.
• You are good at managing evolving priorities while successfully delivering core initiatives. 
• You have experience working with collaborators across large time zone differences.

• This role is hybrid (4 days a week in one of our SF Bay Area offices)