Data Science Lead, Glean

• You have a Bachelors/Masters/PhD degree in Statistics, Mathematics or Computer Science, or another quantitative field
• You have 8+ years of industry experience as a data scientist. For PhD degree holders, the minimum years of required experience as a data scientist is 6 years
• You have strong business sense, and are strong at defining good product KPIs/guardrail metrics, dashboarding and analysis of raw data to derive strategic insights
• You are familiar with BI visualization tools such as Sigma, Metabase, Tableau or Looker
• You are proficient in SQL and the modern data stack (e.g. dbt pipelines for ETL/ELT)
• You are proficient in Python
• You have experience in writing source-controlled code for pipelines and internal tools for data-oriented decision making
• You are strong at statistics. You have experience in applying these skills into tangible improvements in products, internal tools and processes using A/B testing and non-experimental methods
• You are concise and precise in written and verbal communication. Technical documentation is your strong suit

Desirable

• At Glean, data scientists collaborate with engineering, product management and design to
• Define and build data assets, e.g. KPI definitions, data pipelines and dashboards, to measure the performance of AI-powered assistant products for knowledge workers
• Identify opportunities to improve these KPIs, and influence cross functional teams to incorporate associated changes into their roadmaps
• Create and maintain quantitative frameworks and methodologies, e.g. bring more rigor into experiment analyses, use statistical modeling to identify leading indicators of user growth and engagement
• If you are up to it, this role would span a large product portfolio that aims to make Glean mission critical and delightful for the knowledge worker (i.e. the user) and the firm she works at (i.e. the customer)
• It explores how Glean’s search and generative AI products should intersect
• It explores different product modalities like web, mobile and desktop apps, as well as experiences where Glean’s embedded into other internal and external services a customer uses
• It explores how unstructured data in documents, structured data and data outside of an organization should come together
• It looks at the knowledge worker as a potential creator of generative AI experiences for others around her, rather than a mere consumer of these experiences
• It empathizes knowledge workers in specific job function verticals
• It seeks to empower Glean’s customers to reign in the proliferated set of AI agents around them for maximum value, whether or not they are created by Glean
• At the intersection of all these domains is an obsession about a user and customer’s experience
• Combined with an enterprise-grade & highly performant AI, such magical experience is the prerequisite for making Glean grow into over 1B knowledge workers out there
• This role presents a lot leadership opportunities including:
• Being the main data science point of contact for multiple high-profile initiatives to collaborate with various ENG, product management and design XFNs
• Tech-leading other data scientists working in similar domains
• If you deliver strong results as an IC leader, this role would also evolve into tech lead management as well. You’ll always stay hands on though, just all Gleanies
• Making sure that your data science pod works cohesively with other data science pods as well as other data roles like data engineering, business intelligence and applied science
• Presenting important strategic insights & wins to executive leadership