MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data.
We enable organizations of all sizes to easily build, scale, and run modern applications by helping them modernize legacy workloads, embrace innovation, and unleash AI.
Our industry-leading developer data platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available in more than 115 regions across AWS, Google Cloud, and Microsoft Azure.
Atlas allows customers to build and run applications anywhere—on premises, or across cloud providers.
With offices worldwide and over 175,000 new developers signing up to use MongoDB every month, it’s no wonder that leading organizations, like Samsung and Toyota, trust MongoDB to build next-generation, AI-powered applications.
The Staff Data Scientist will play a critical role in driving broad impact at MongoDB through researching, prototyping and integrating predictive solutions into systems across the company.
As a member of the Business Data Science team, the Staff Data Scientist will partner closely with other teams including Go To Market, Sales, Product, and IT to further our team mission: Delivering fit-for-purpose, practical, and production-ready solutions to drive business outcomes and operational efficiencies through AI/ML model development and statistical thought partnership.
We are growing adoption of data science models across the business, and you will help us increase this velocity substantially.
This role can be based out of our New York City or Austin offices or remotely in the United States.
As one of the most senior technical members of the centralized Data Science team, this role will have key externally- and internally-facing leadership responsibilities, including:
Responsibilities
Represent the MongoDB Data Science team to business stakeholders across different verticals, navigating the translation layer between business unit requirements and cutting-edge technical solutionsProactively propose new solutions based on business priorities derived from close collaboration with product managers and other engineersOwn end-to-end implementation of data science projects (feasibility analysis, data preparation, feature engineering and model evaluation, as well as deployment and maintenance) Provide thought leadership to the broader organization on the best way of using machine learning technologies, frameworks and approaches to solve different problemsEvangelize appropriate tech and engineering best practicesDefine and improve business & product metrics to optimize the quality and cost of AI usageMaintain a high standard of operational excellence across the team by streamlining workflows and acting as a core code contributor to internal packages, tooling, and team processesPartner with DS leadership to strategize initiatives, identifying opportunities for other members of the team to contribute to complex workstreams, understanding teammates' skillsets and growth potentialAnticipate opportunities and obstacles across the team, and invest ahead of problems to ensure continuity (and growth where possible) of service.
Balance maintenance investment based on project ROI Skills & Attributes
8+ years of hands-on data science and machine learning model developmentProven track record of coordinating and collaborating effectively with stakeholders and end-users of data science products to ensure the delivery of fit-for-purpose solutionsEmbraces an object-oriented approach to designing scalable and readable Python codebase, and has experience working with engineers on architectural design of machine learning systemsExpertise and track record of working autonomously across the entire data science development lifecycle, including prototyping, simulation, tuning and delivering data science artifacts to production deployment environments with and without dedicated engineering helpAbility to translate efficacy measurements of data science models and products into tangible business impact metricsEffective at communicating technical concepts to non-technical audiences, including business leadershipCommitted to contributing to a collaborative, enjoyable, and psychologically safe work environmentPhD or Masters degree in a quantitative/computational discipline (computer science, applied mathematics, statistics, physics, operations research, etc.)