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Senior Data Scientist, Engineering Systems

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 Senior Data Scientist for Engineering Systems will play a critical role in driving impact at MongoDB through researching, prototyping and integrating machine learning algorithms into systems across the broader engineering organization.  As a member of the Platform Data Science team, the Senior Data Scientist will partner closely with engineering teams such as Core Database Engine, Release Quality, Database Performance Testing, and Developer Productivity teams 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 office or remotely in the United States.

Key responsibilities will include will include:

  • Delivering production ready, thoroughly tested statistical and machine learning algorithms with well-identified limitations 
  • Collaborating cross-functionally with engineers from various teams to iterate on ML service architecture within a production environment
  • Identifying gaps in product coverage and working with Engineering, Data Architecture and Data Engineering teams to collect, transform and use novel data sets effectively

(Additional) Responsibilities

  • Delivering thoughtful and kind code reviews to your fellow team members and mentoring more junior data scientists
  • Collaborating with Engineering, Product Analytics and Product Management on identifying promising areas for applying machine learning algorithms
  • Communicating, coordinating, and collaborating effectively with stakeholders  and taking a proactive role in shaping new relationships with Engineering teams
  • Acting as a core code contributor to internal packages, tooling and team processes

Skills & Attributes

  • 5+ years of hands-on machine learning model development 
  • 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
  • Expertise and track of record 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 help
  • Effective at communicating technical concepts to non-technical audiences; e.g. able to translate efficacy measurements of data science models and products into tangible business impact metrics
  • Committed to contributing to a collaborative, enjoyable, and psychologically safe work environment
  • Master's degree or equivalent experience in a quantitative/computational discipline (computer science, applied mathematics, statistics, physics, operations research, etc.)

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, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 1263109139

MongoDB’s base salary range for this role is posted below. Compensation at the time of offer is unique to each candidate and based on a variety of factors such as skill set, experience, qualifications, and work location. Salary is one part of MongoDB’s total compensation and benefits package. Other benefits for eligible employees may include: equity, participation in the employee stock purchase program, flexible paid time off, 20 weeks fully-paid gender-neutral parental leave, fertility and adoption assistance, 401(k) plan, mental health counseling, access to transgender-inclusive health insurance coverage, and health benefits offerings. Please note, the base salary range listed below and the benefits in this paragraph are only applicable to U.S.-based candidates.

MongoDB’s base salary range for this role in the U.S. is:
$126,000$248,000 USD

Average salary estimate

$187000 / YEARLY (est.)
min
max
$126000K
$248000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior Data Scientist, Engineering Systems, MongoDB

If you're a passionate data enthusiast with a knack for machine learning, then the Senior Data Scientist position at MongoDB might just be your next adventure. At MongoDB, we're on a mission to empower innovators with the tools they need to disrupt industries through the power of software and data. As a key member of the Platform Data Science team, you'll dive deep into researching and integrating machine learning algorithms that directly impact our engineering systems. You’ll collaborate closely with various engineering teams like Core Database Engine and Database Performance Testing to develop practical, production-ready solutions that can drive real business outcomes. We’re looking for someone with at least 5 years of hands-on experience in machine learning model development, who is proficient in Python and is comfortable translating technical jargon into business impact. This role offers a flexible work environment, whether you prefer the buzz of our New York City office or the comfort of your home. Here, you’ll not only sharpen your skills but also help mentor junior data scientists and build valuable cross-functional relationships. At MongoDB, we value your growth! Enjoy generous benefits, including flexible paid time off and extensive parental leave, in addition to a competitive salary ranging from $126,000 to $248,000 based on your experience. Come help us shape the future of technology!

Frequently Asked Questions (FAQs) for Senior Data Scientist, Engineering Systems Role at MongoDB
What are the main responsibilities of the Senior Data Scientist at MongoDB?

The Senior Data Scientist at MongoDB is responsible for delivering production-ready statistical and machine learning algorithms, collaborating with engineering teams to enhance ML service architecture, and identifying gaps in product coverage. The role involves working with cross-functional teams on data sets and mentoring junior data scientists.

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What qualifications are needed to apply for the Senior Data Scientist position at MongoDB?

To apply for the Senior Data Scientist role at MongoDB, candidates should possess 5+ years of experience in machine learning model development, a Master's degree or equivalent experience in a quantitative discipline, and proficiency in Python. Effective communication skills to convey technical concepts to non-technical audiences are also essential.

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How does the Senior Data Scientist collaborate with engineering teams at MongoDB?

The Senior Data Scientist collaborates cross-functionally with teams such as the Core Database Engine and Database Performance Testing at MongoDB to prototype and integrate machine learning models. By understanding various engineering perspectives, they can craft solutions that meet operational needs effectively.

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What is the work culture like at MongoDB for a Senior Data Scientist?

MongoDB fosters a supportive and enriching culture for its employees. The environment emphasizes collaboration, psychological safety, and personal growth, making it a great place for a Senior Data Scientist to thrive, contribute, and mentor others.

Join Rise to see the full answer
What career growth opportunities are available for a Senior Data Scientist at MongoDB?

A Senior Data Scientist at MongoDB will have numerous opportunities for career growth, including mentoring junior team members, leading innovative projects, and enhancing technical skills in machine learning and data science, all while contributing to impactful business solutions.

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Common Interview Questions for Senior Data Scientist, Engineering Systems
Can you describe your experience with machine learning model development?

While answering this question, highlight specific projects where you've developed machine learning models. Discuss the algorithms used, the challenges faced, and how your models drove business outcome transformations. Be sure to reflect on your role in the entire lifecycle of model development.

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How do you approach problem-solving when faced with gaps in product coverage?

Share your systematic approach to identifying gaps in product coverage. Discuss how you collaborate with engineering and data architecture teams to gather insights and propose solutions. Include a real-life example where you successfully addressed a similar challenge.

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What programming languages are you proficient in, and how have you used them in your work?

Specify your proficiency in programming languages, notably Python, and provide examples of how you've used them to build scalable and readable code. Emphasize any projects where effective coding led to improved collaboration with engineering teams.

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How do you ensure your machine learning algorithms are production-ready?

Discuss your step-by-step process for testing and validating machine learning algorithms before deployment. Talk about the importance of identifying limitations and gathering feedback from stakeholders to improve model efficacy.

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How do you communicate complex technical concepts to non-technical stakeholders?

Provide examples of how you've successfully communicated technical ideas to non-technical audiences. Discuss your strategy for simplifying complex information and ensuring your audience understands the business impact of your work.

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Can you share a successful project where you implemented machine learning in a production environment?

Outline a detailed scenario where you played a critical role in implementing machine learning in production. Explain the context, your responsibilities, the technical challenges you overcame, and the positive results that ensued.

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What role do statistics play in your machine learning projects?

Describe the statistical methods you employ in model development. Discuss how you utilize statistical principles to analyze data, validate models, and ensure your solutions are both robust and reliable.

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How do you keep up with the latest trends and technologies in data science?

Talk about the sources you rely on for updates in data science, such as online courses, webinars, or industry publications. Highlight your proactive approach to continuous learning and incorporating new methodologies into your work.

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What is your experience with mentoring junior data scientists?

Share your experiences mentoring junior data scientists. Discuss your philosophy on mentorship, approaches to guiding them through complex projects, and the benefits you've noticed in team dynamics as a result.

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Why do you want to work for MongoDB as a Senior Data Scientist?

Highlight your values aligning with MongoDB's innovative mission. Share your enthusiasm for contributing to impactful projects, the supportive culture, and how your expertise directly contributes to their goals in technology and data.

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MongoDB empowers innovators to create, transform, and disrupt industries by unleashing the power of software and data.

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DATE POSTED
April 2, 2025

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