Senior Data Scientist

Partner with Server Query, Atlas Clusters, Release Quality and other engineers to embed algorithmic rigor and optimization into resource scaling, release-safety and monitoring systems across the fleet and inside query engine

Deliver production-ready, thoroughly tested statistical and ML algorithms with well-identified limitations that deliver measurable business impact, not just an impressive-sounding methodology

Own the full feedback loop: instrument model architecture with the metrics needed to track performance and create dashboards in collaboration with our stellar analytics team, collect feedback from users and metrics to diagnose issues or opportunities, and iterate accordingly

Deliver thoughtful, kind code reviews to your peers and act as a core contributor to internal packages, tooling, and team processes that increase developer productivity

In 3 months, you’re familiar with our workflow, have an elementary understanding of our product and what teams we work with. You have delivered small-to-medium improvements to our project portfolio

In 6 months, you’ve delivered one feature you researched and prototyped from scratch and demonstrated its impact on business metrics of your choice

In 12 months, you've established a track record of shipping ML-driven improvements to fleet stability, efficiency, or operational automation; deepened working relationships with two or more partner engineering teams; and become a go-to resource for statistical or engineering rigor across the team

5+ years of hands-on machine learning model development, working directly with technical stakeholders

Expertise and track of record working autonomously across the entire machine learning development lifecycle, including prototyping, simulation, tuning and iterating on products in deployment environments with and without dedicated engineering help