Data Science Engineer

MrBeast • San Francisco, CA

1d • Hybrid

About The Position

We're undertaking an AI-first engineering rebuild for a company with an existing audience of over 100 million people. This is a ground-up development, free from legacy constraints, allowing the models and data systems you implement to form the core foundation rather than being patches on an existing structure. Your role will be to transform ambiguous, high-stakes business challenges into practical models that demonstrably improve key metrics in a production environment. You will serve as the senior technical lead for a data science domain, responsible for the entire lifecycle of projects, from problem definition through deployment, ongoing monitoring, and iterative improvement. The work will encompass consumer products, media, and fintech analytics, all leveraging an audience base exceeding 100 million individuals.

Requirements

• AI-Native: You're already burning through tokens and using AI in your daily workflow to move faster from idea to shipped model.
• Production ML Builder: Typically 8+ years designing, building, and deploying ML models in production, with deep expertise in statistical modeling and sound judgment about method selection under uncertainty.
• End-to-End Owner: You've owned problems start to finish with limited supervision and been accountable for the result, not just the experiment.
• Honest Communicator: You frame problems as testable hypotheses, hold the line on validation rigor under deadline pressure, and communicate uncertainty honestly instead of overselling.
• Strong software engineering practice: production-quality code, version control, testing, and reproducible pipelines.

Nice To Haves

• Setting technical direction for a data science domain
• MLOps tooling for deployment and monitoring
• Domain exposure in consumer products, media, or fintech

Responsibilities

• Own the full model lifecycle: data sourcing and quality, features, training, evaluation, deployment, monitoring, and retraining.
• Set and enforce the domain's standards for validation, reproducibility, experimentation, and monitoring.
• Partner with engineering to productionize models reliably, with the right latency, scale, and observability.
• Translate model behavior and its limits for product and business stakeholders, including where data science can't help.
• Anticipate the failure modes (leakage, drift, bias, fragility) and build safeguards before they reach production.
• Guide the technical work of other data scientists and engineers through design review, pairing, and mentorship.
• Evaluate and adopt new methods and tooling, weighing innovation against maintainability and cost.

Benefits

• Equity: Highly competitive equity package designed for a foundational hire.
• Competitive Salary
• Generous Medical (Blue Cross Blue Shield), Dental, Vision and company-paid Life Insurance
• Company contributions to employee Health Savings Accounts (HSA)
• 401k Plan with Safe Harbor company-matching
• Flexible vacation policy and paid company holidays
• Company-provided technology package
• Relocation assistance where applicable, including travel and company-provided housing for the first 90 days

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed