Senior Data Science Engineer Jobs in San Francisco, CA
Senior Data Science Engineer jobs in San Francisco are concentrated in SoMa, the Financial District, and Mission Bay, driven by demand from tech platforms, fintech firms, and life sciences companies. Employers hiring right now include Adobe, Lyft, and GoFundMe. See the openings below and apply to the ones that match your experience.
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About us
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.
Data Science Engineer
Primary: Bay Area (San Francisco / Peninsula) | Secondary: NYC
The Opportunity
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.
The Product
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.
Who You Are
- 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.
Benefits
- Equity: Highly competitive equity package designed for a foundational hire.
- Hybrid Model: Expected: 3 days per week in-office (Bay Area or NYC).
The Perks — Why Work On the MrBeast Team
We are redefining what entertainment and storytelling look like at global scale. Every piece of content we publish reaches millions and influences culture in real time. This is your opportunity to join the team that decides how those moments come to life across every screen.
- 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
Come build the future of the creator ecosystem with us.
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Find JobsSenior Data Science Engineer Job Market in San Francisco
Who's Hiring
- Adobe5

- Lyft3

- GoFundMe2

- Genentech2

- Stitch Fix2

Top Industries Hiring
- Technology & Software21
- Retail3
- Fintech2
- Medical Devices2
- Distribution & Wholesale2
Senior Data Science Engineer Jobs in San Francisco: Frequently Asked Questions
How do I get a senior data science engineer job in San Francisco?
Focus your search on SoMa, Mission Bay, and the Financial District, where tech platforms, fintech firms, and biotech companies concentrate their data science teams. Candidates who stand out in San Francisco typically combine production-level ML experience with strong Python or Scala skills and can show measurable business impact. Networking through local data science meetups and contributing to open-source projects visible to Bay Area employers also gives you a concrete edge.
Which companies hire senior data science engineers in San Francisco?
Employers hiring senior data science engineers in San Francisco right now include Adobe, Lyft, and GoFundMe, based on current listings on Migrate Mate as of June 2026. San Francisco's market includes a broad mix of late-stage startups, established tech giants, and financial services firms, all maintaining active data science teams.
Are there remote senior data science engineer jobs in San Francisco?
Yes, senior data science engineer roles are well-suited to remote and hybrid arrangements because the work is primarily analytical and code-based rather than on-site. About 67% of senior data science engineer openings tied to San Francisco are remote or hybrid as of June 2026, reflecting the tech sector's continued flexibility. Modeling, experimentation, and pipeline development are the functions most commonly performed remotely by San Francisco-based teams.
How can I get a senior data science engineer job in San Francisco with little or no experience?
The most realistic entry path in San Francisco is securing a junior data scientist or data analyst role at a mid-size tech company in SoMa or the Financial District and building toward senior-level ownership within two to three years. Early-career candidates gain traction by contributing to well-documented projects on GitHub, completing relevant Kaggle competitions, or landing contract work through San Francisco's active startup ecosystem. Targeting growth-stage startups, which often promote quickly, can accelerate your path to a senior title.
Which industries hire the most senior data science engineers in San Francisco?
Most senior data science engineer openings in San Francisco sit in Technology & Software, Retail, and Fintech, per current listings on Migrate Mate as of June 2026. San Francisco's density of tech platforms, fintech companies, and life sciences organizations drives consistent demand for senior data science talent across these sectors.
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