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
- 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
- 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.)
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 builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud data platform and is available across AWS, Google Cloud, and Microsoft Azure.
With offices worldwide and over 67,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.
Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB.
To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!
MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.
MongoDB is an equal opportunities employer.
Req ID: 3273521781
Skills Required
- 5+ years of hands-on machine learning model development experience working with technical stakeholders
- Experience across the full machine learning development lifecycle, including prototyping, simulation, tuning, deployment, and iteration
- Experience designing scalable, readable, object-oriented Python codebases
- Experience collaborating with engineers on machine learning system architecture
- Ability to review, understand, and redesign production code
- Ability to communicate technical machine learning concepts and model efficacy to non-ML audiences
- Master’s degree or equivalent experience in a quantitative or computational discipline
What the Team is Saying
MongoDB Compensation & Benefits Highlights
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Parental & Family Support — Parental leave is set at 20 weeks paid and gender‑neutral (generally after one year of service), alongside 90% reimbursement for fertility, adoption, or surrogacy costs up to $50,000. Backup child/elder care via Care.com and parenting support (e.g., Cleo) add depth to family coverage.
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Healthcare Strength — Medical, dental, and vision options are broad, with some plans listing $0 employee‑only premiums, HSA contributions, and a free One Medical membership. Mental health resources include Spring Health (with therapy visits), an EAP, and a trained Mental Health First Aid network.
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Equity Value & Accessibility — Total rewards commonly include RSU equity grants and an Employee Stock Purchase Plan, creating meaningful ownership opportunities. Multiple role descriptions and benefits pages emphasize equity as a core component of compensation.
MongoDB Insights
What We Do
The database market is big. How big? Well, according to IDC, it’ll reach $153 billion by 2027. And MongoDB is at the forefront of that innovation with thousands of customers across the globe. We empower developers and businesses to build and deploy the applications they want, wherever they want.
Why Work With Us
We are ambitious. We are passionate about creativity. And we believe the best paths are the ones we have yet to forge.
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MongoDB Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
MongoDB provides multiple working model options for our employees, including the flexibility to work from home to opportunities for collaboration and social interaction in a MongoDB office.
