Senior Applied Scientist: A/B Testing & ML Platforms

Glean; Mountain View (CA); Hybrid; USD 180,000 - 330,000; Full time

Benefits offered by this job

Home office stipend

Education stipend

Wellness stipend

Healthy lunches daily

Job summary

Glean is hiring for an applied science role in San Francisco focusing on data organization and experimentation. You will work with product data science and engineering to improve online A/B testing, build scalable evaluation tools, and drive data-driven product decisions.

The role requires 5+ years post-master’s or 3+ years post-PhD in statistics, math, or CS, with strong Python and SQL skills, and experience in B2B SaaS. Hybrid work in the SF Bay Area is offered with competitive compensation.

Qualifications

• 5+ years with a Master’s degree in a quantitative field.
• 3+ years with a PhD in a quantitative field.
• Strong statistics and machine learning applied to product metrics.

Responsibilities

• Collaborate with product data science and engineering on online A/B experiments.
• Develop and maintain an A/B experimentation platform.
• Deploy techniques to production with scalable tooling.
• Evaluate hero use cases like document/URL uploads and generate evaluation criteria.
• Break down evaluations into granular tasks like summarization, multi-step reasoning, tool selection, and system design.

Skills

Python

SQL

A/B testing

Statistics

Machine learning

Education

Master's degree in Statistics / Mathematics / CS

PhD in Statistics / Mathematics / CS

Tools

dbt

Data visualization

Job description

Glean is hiring for an applied science role in San Francisco focusing on data organization and experimentation. You will work with product data science and engineering to improve online A/B testing, build scalable evaluation tools, and drive data-driven product decisions.

The role requires 5+ years post-master’s or 3+ years post-PhD in statistics, math, or CS, with strong Python and SQL skills, and experience in B2B SaaS. Hybrid work in the SF Bay Area is offered with competitive compensation.