Data Science Lead, Glean

• You have a Bachelors/Masters/PhD degree in Statistics, Mathematics or Computer Science, or another quantitative field
• You have 6+ years of industry experience (4+ for Masters degree holders, 3+ for PhD degree holders)
• You are concise and precise in written and verbal communication. Technical documentation is your strong suit
• You are strong at defining good product KPIs/guardrail metrics, dashboarding and analysis of raw data to derive strategic insights
• You are proficient in SQL and the modern data stack (e.g. dbt pipelines for ETL/ELT)
• You have experience in writing source-controlled code for pipelines and internal tools for data-oriented decision making
• You’re strong in statistics and/or machine learning. You have experience in applying these skills into tangible improvements in products, internal tools and processes
• You are proficient in Python

Desirable

• Glean is building a world-class Data Organization composed of data science, applied science, data engineering and business intelligence groups. This is a data science role based in our Palo Alto headquarters
• 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 churn & retention
• This specific role will address one or more of the following business domains:
• Define KPIs, create data foundations, build visualizations and devise techniques to measure the efficacy of Glean’s world-leading document indexing, retrieval and ranking infrastructure, which powers its search and LLM-powered products. Conduct rigorous empirical analyses and influence ML ENG & ENG to improve this infrastructure
• Collaborate with product management and product ENG teams to improve the way Glean measures interactions with its Search product, iterating on our KPIs, data foundations, dashboards and measurement techniques accordingly. Identifying opportunities and influencing ENG, design and product management XFNs to improve value-add of a user’s experience with Search vis a vis other LLM-based and general knowledge management products Glean offers