Data Science Engineer Jobs
Data Science Engineer jobs are open across technology, healthcare, finance, and e-commerce, from entry-level to principal and staff levels, with specializations in machine learning infrastructure, MLOps, and real-time data pipelines. Find a role that fits from the openings below and apply directly.
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Showing 5 of 12,413+ Data Science Engineer jobs
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.
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.
See All 12,413+ Data Science Engineer Jobs
Jump back to the full list of openings and apply to any data science engineer role that fits.
Find Data Science Engineer JobsData Science Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Apple544
- Amazon465
- NVIDIA381
- Tata Consultancy Services (TCS)226
- TikTok193
Top Industries Hiring
- Technology & Software4,969
- Electronics & Hardware999
- Consulting & Professional Services939
- Banking & Financial Services679
- Investment & Asset Management478
What Employers Look For
The qualifications that appear most often in data science engineer jobs.
- Proficiency in Python with experience building production-grade data and ML pipelines
- Hands-on experience with distributed computing frameworks such as Apache Spark or Flink
- Experience deploying and monitoring machine learning models in cloud environments like AWS, GCP, or Azure
- Familiarity with MLOps tooling including feature stores, model registries, and workflow orchestrators
- Bachelor's or master's degree in computer science, data engineering, statistics, or a related field
- Experience with containerization and orchestration tools such as Docker and Kubernetes
Tips for Your Data Science Engineer Job Search
Separate your ML and engineering work
Hiring managers want to see both sides of your work. Structure your resume into distinct sections for model development and for infrastructure, pipelines, or deployment engineering. Conflating the two makes it harder for reviewers to assess either.
Target openings that match your stack
Data science engineer job descriptions vary widely in tooling. Filter for roles that name the exact frameworks you know well, whether that is PyTorch, Spark, or Kubeflow. Applying to a strong stack match beats applying broadly to a dozen misaligned roles.
Apply early to roles that fit
Migrate Mate lists data science engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Showcase end-to-end project ownership
Interviewers routinely ask how you took a model from prototype to production. Prepare a clear narrative for at least one project covering data ingestion, model training, deployment, and monitoring. Vague answers about 'building models' without the deployment story consistently hurt candidates at this level.
Quantify pipeline performance, not just accuracy
Accuracy metrics impress data scientists, but data science engineers are expected to own system performance too. Lead with latency improvements, throughput gains, or cost reductions your pipelines achieved. Those numbers stand out to engineering managers who are evaluating your systems thinking.
Negotiate on scope, not just compensation
After an offer, ask specifically which part of the ML lifecycle you will own at hire versus six months in. Scope ambiguity is the top source of dissatisfaction in this role. Clarifying it upfront also signals the kind of systems-level thinking that makes hiring managers more confident in their offer.
Data Science Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data science engineers?
The companies hiring the most data science engineers right now include Apple, Amazon, and NVIDIA, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of June 2026. Demand is especially concentrated in technology, financial services, and healthcare data platforms.
How many data science engineer jobs are remote?
About 28% of data science engineer openings are fully remote or hybrid as of June 2026, making it one of the more flexible engineering roles to search. Sub-areas like MLOps, feature engineering, and model serving infrastructure tend to have the highest share of remote-eligible positions, since those workflows are well-suited to asynchronous, distributed team structures.
How do you become a data science engineer?
Start by building strong fundamentals in Python and SQL, then layer in distributed data processing frameworks like Spark. Work on end-to-end projects that take a model from raw data through training, deployment, and monitoring. Develop familiarity with cloud platforms and containerization. Contributing to open-source data infrastructure projects or publishing documented pipelines on a public portfolio accelerates your path into the role.
Can you break into data science engineering without much experience?
Yes, but you need to substitute depth for breadth early on. Build one well-documented end-to-end ML pipeline project that demonstrates data ingestion, training, deployment, and observability. Roles titled junior data engineer or ML engineer are common entry points where companies expect less production experience. Certifications in a major cloud platform can strengthen applications when your professional history is thin.
What does the data science engineer interview process look like?
Most processes run three to five rounds. An initial recruiter screen is followed by a technical phone interview covering Python, SQL, or system design basics. The core rounds typically include a take-home or live coding assessment on pipeline design, a machine learning systems design interview, and a final round with engineering leadership. Some employers add a presentation of a past project as a fifth stage.
Where can I find and apply to data science engineer jobs?
You can find and apply to data science engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Find the roles that fit your experience and stack, then apply directly to each listing from the results on this page.
See All 12,413+ Data Science Engineer Jobs
Jump back to the full list of openings and apply to any data science engineer role that fits.
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