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.

We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model.

What You'll Do

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

Measures of Success

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

Skills & Attributes

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
Our codebase is primarily in Python and we use AI for developer productivity - but we expect all ICs to review, understand, and be able to redesign any code that ships regardless of whether a human or an AI wrote the first draft
Takes ownership of team culture: models psychological safety, and - in whatever way suits their style, whether that's a quiet word or a vocal challenge - encourages others to speak up and calls out when the environment isn't living up to it
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.)

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling