About the job
As Senior Data Scientist for Engineering Systems you will work independently alongside sharp, generous, and pragmatic engineers from Server Query, Atlas Clusters, and Release Quality, among other teams. Together, we tackle problems spanning resource scaling across the Atlas fleet, safe feature rollout to MongoDB clusters, automated incident response and query engine performance. Join the Platform Data Science team and help us research, prototype and ship machine learning features for MongoDB’s core server, query engine and Atlas, our database-as-a-service cloud offering.
Responsibilities
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
Qualifications
Minimum
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
Embraces an object-oriented approach to designing scalable and readable Python codebase, and has experience working with engineers on architecture design of machine learning systems
Able to review, understand, and redesign any code that ships regardless of whether a human or an AI wrote the first draft
Effective at communicating technical ML concepts to non-ML-experts audiences; e.g. able to translate efficacy measurements of ML models and products into tangible business impact metrics
Master's degree or equivalent experience in a quantitative/computational discipline (computer science, applied mathematics, statistics, physics, operations research, etc.)
Preferred
No preferred qualifications listed.