Data Science Engineer

About the Company

Beast Industries is a media and entertainment company built by Jimmy Donaldson, widely recognized as MrBeast. The organization spans digital media, philanthropy, consumer products, and other new ventures, with a goal of creating entertainment that also informs, inspires, and makes a real-world impact.

Role Overview

This position sits at the center of an AI-led engineering rebuild for a business with an audience of more than 100 million people. It is a ground-up opportunity with no inherited legacy systems, so the models and data platforms you create will shape the core foundation of the company. The focus is on converting unclear, high-impact business challenges into data science solutions that drive measurable results in production.

The role is primarily based in the Bay Area, specifically San Francisco or the Peninsula, with New York City as a secondary location. A hybrid schedule is expected, with three in-office days each week.

Key Scope

You will act as the senior technical lead for a data science area, managing the complete lifecycle from problem definition through deployment, monitoring, and ongoing improvement. The work touches consumer products, media, and fintech analytics at large scale.

• Convert ambiguous business questions into practical data science problems and determine the right modeling approach and success metrics when there is limited prior precedent.
• Design, develop, and deploy models along with the data pipelines required to train and serve them in production.
• Create monitoring and retraining systems that detect model drift before it impacts business outcomes.

Responsibilities

• Own the full model lifecycle, including data sourcing, data quality, feature creation, training, evaluation, deployment, monitoring, and retraining.
• Define and uphold standards for validation, reproducibility, experimentation, and monitoring within the domain.
• Work closely with engineering teams to ship models reliably with the right latency, scalability, and observability.
• Explain model behavior, constraints, and limitations to product and business stakeholders, including when data science is not the right solution.
• Identify likely failure points such as leakage, drift, bias, and fragility, and put guardrails in place before release.
• Support and mentor other data scientists and engineers through design reviews, collaboration, and hands-on guidance.
• Assess emerging techniques and tools, balancing innovation with long-term maintainability and cost.

Requirements

• Strong hands-on use of AI tools in everyday work, with a habit of using them to accelerate the path from idea to deployed model.
• Roughly 8+ years of experience designing, building, and deploying machine learning systems in production.
• Deep knowledge of statistical modeling and strong judgment for choosing methods under uncertainty.
• Proven ability to own complex problems from start to finish with limited supervision and accountability for outcomes.
• Clear and honest communication style, with the ability to frame work as testable hypotheses and avoid overstating certainty.
• Solid software engineering discipline, including production-grade code, version control, testing, and reproducible pipelines.
• Nice-to-have experience in technical leadership for a data science function, MLOps tooling for deployment and monitoring, or work in consumer products, media, or fintech.

Benefits

• Highly competitive equity as part of a foundational hire.
• Hybrid work setup with an expectation of three days per week in the office in the Bay Area or New York City.

Perks

• Competitive compensation.
• Medical, dental, and vision coverage, plus company-paid life insurance.
• Employer contributions to Health Savings Accounts.
• 401(k) plan with Safe Harbor matching.
• Flexible vacation policy and paid company holidays.
• Company-provided technology equipment.
• Relocation support where eligible, including travel and company-paid housing for the first 90 days.

Additional Information

This role is described as a foundational hire in a zero-to-one environment where your work will directly influence the company’s future systems and products. The organization emphasizes creativity, innovation, and social impact, and values people who can help shape the next generation of its creator ecosystem.