Data Science Engineer

Beast Industries is a multifaceted media and entertainment company founded by Jimmy Donaldson, popularly known as MrBeast, the most watched person in the world. Renowned for revolutionizing digital content creation, Beast Industries encompasses a diverse portfolio of ventures that extend far beyond its origins on YouTube. With a mission to entertain, inspire, and create significant social impact, Beast Industries operates across various domains including digital media, philanthropy, consumer products, and innovative business initiatives. At Beast Industries, we believe in the transformative power of digital media and its potential to entertain, educate, and effect positive change. Our commitment to innovation, creativity, and philanthropy drives us to explore new frontiers, create unforgettable experiences, and build a legacy that inspires future generations.

Primary: Bay Area (San Francisco / Peninsula)   |   Secondary: NYC

We're doing an AI-first engineering rebuild for a company that already has an audience of 100M+ people. This is a zero-to-one build with no legacy constraints, so the models and data systems you ship define the foundation instead of patching an old one. You're here to turn ambiguous, high-stakes business problems into models that actually move a number in production.

You'll be the senior technical anchor for a data science domain, owning the full lifecycle from framing the problem through deployment, monitoring, and iteration. The work spans consumer products, media, and fintech analytics, all sitting on top of an audience of 100M+ people. That means:

• Frame vague business problems as tractable data science problems, and pick the approach and evaluation criteria when there's little precedent.
• Design, build, and deploy models and the data pipelines that feed and serve them in production.
• Build the monitoring and retraining framework that catches drift before it hits the business.

• →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.

• 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. Bonus points for setting technical direction for a data science domain, MLOps tooling for deployment and monitoring, and domain exposure in consumer products, media, or fintech.