H-1B Visa Senior Staff Data Scientist Jobs
Senior Staff Data Scientists command some of the strongest H-1B visa sponsorship rates in tech, with roles classified under SOC codes that consistently clear the specialty occupation bar. Employers filing LCAs for this level typically sponsor at all three H-1B wage tiers, and premium processing is common given the seniority and business-critical nature of the work.
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Get Access To All JobsAbout Grubhub
At Grubhub, we believe food is more than just a meal: It’s a source of discovery, connection, and pure enjoyment. There’s a time and place for every type of dish, from hidden neighborhood gems to tried-and-true favorites, and we exist to connect people with the food they love in all the ways they like to dig in. We’ve been at it since 2004, but now, as part of Wonder, Grubhub is operating with a renewed sense of momentum and the high-velocity energy of a powerhouse startup.
As a leading U.S. ordering and delivery marketplace, we feature over 415,000 merchants in more than 4,000 cities, creating the ultimate food experience by elevating online ordering through innovative restaurant technology, easy-to-use platforms, and an improved delivery experience. We are constantly finding new ways to innovate—from integrated grocery delivery with groceries powered by Instacart to exclusive loyalty programs. Join our team, based out of New York City and Chicago, and help us give our diners the exceptional value they deserve.
About the Opportunity
At Wonder Data Science, our mission is to build data science and machine learning systems that improve how our marketplace operates, how customers experience the platform, and how the business makes high-quality decisions. As a Senior Staff Data Scientist, you will go beyond individual problem solving — you will help shape the strategic direction of applied data science, mentor senior and junior scientists, and collaborate closely with engineering, product, operations, and business leaders to move our ML and analytics capabilities toward scalable, production-grade systems.
You will identify high-leverage opportunities across the business, including marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing strategy, supply planning, demand forecasting, and operational performance. You will design statistically rigorous frameworks to understand causal impact, separate signal from noise, and guide business strategy through experimentation, measurement, and principled inference.
You will help define how we structure trade-offs like customer experience vs. operational efficiency, speed vs. cost, prediction accuracy vs. business impact, short-term metric movement vs. long-term marketplace health, and automation vs. human judgment. You’ll prototype, experiment, influence architecture, and ensure we operationalize models and insights that actually move business metrics — not just analyses that look good offline.
The Impact You Will Make
- Serve as a technical thought leader in Data Science — defining principles, frameworks, and best practices for how Wonder uses data, experimentation, and machine learning to improve customer, marketplace, and business outcomes.
- Mentor and coach a growing team of Data Scientists and contribute to career development and technical excellence across the group.
- Lead the exploration of interconnected marketplace systems, recognizing feedback loops between customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
- Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact.
- Partner with engineering to drive architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
- Define and implement robust experimentation strategies for changes that move business metrics in high-noise environments.
- Champion business-impact-driven data science, integrating causal inference, experimentation, risk-aware modeling, and scalable production ML systems that learn and adapt.
What You Bring to the Table
- 8+ years of industry experience with MS or 6+ years with PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field.
- Proven experience applying data science and machine learning to complex business problems, such as marketplace optimization, customer experience, forecasting, personalization, pricing, supply/demand balancing, operational policy changes, or product experimentation.
- Deep expertise in causal inference, experimentation, and statistical modeling, including methods such as A/B testing, difference-in-differences, regression discontinuity, instrumental variables, synthetic controls, uplift modeling, or causal impact analysis.
- Strong intuition for business and product trade-offs — customer experience vs. efficiency, ETA confidence vs. conversion risk, fulfillment reliability vs. cost, marketplace growth vs. quality, and short-term optimization vs. long-term health.
- Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design.
- Demonstrated ability to take data science, ML, or causal inference systems into production, partnering with engineering on architecture, deployment, and monitoring best practices.
- Fluency in SQL or similar tools for directly interrogating production-scale datasets.
- Experience mentoring and providing technical direction to other scientists, analysts, or engineers.
Got These? Even Better
- Experience leading end-to-end design of data science, machine learning, measurement, or experimentation frameworks within marketplace, consumer product, fulfillment, logistics, pricing, forecasting, or operations systems.
- Experience designing causal measurement strategies for complex systems where product, marketplace, and operational decisions interact across multiple layers.
- Background in causal inference, econometrics, Bayesian modeling, experimental design, or observational measurement in high-noise environments.
- Experience with applied experimentation frameworks, including A/B testing, power analysis, heterogeneous treatment effects, guardrail metrics, interference effects, and long-term impact measurement.
- Experience building or influencing production ML systems that combine predictive modeling, causal measurement, experimentation, and business rules.
- Influence across disciplines — able to align product, engineering, operations, business, and data science around a cohesive ML, experimentation, and measurement strategy.
- Experience defining strategy and technical roadmaps for data science, machine learning, experimentation, or causal inference platforms.
Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in-person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.
Location:
New York: $240,000 - $249,500 per year.
Illinois: $216,000 - $224,500 per year.
Wonder uses geographic-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.
Benefits
The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer a choice of medical, dental, and vision plans, company paid short and long term disability coverage, paid time off including flexible time off for exempt employees, paid vacation for non-exempt employees, and paid sick leave in compliance with applicable law in addition to paid parental leave, discounted meals and exclusive perks across the Wonder family of brands.
Eligibility, effective dates, and available plan options vary by employment classification and location. To learn more about benefits for this role, visit our Careers page here.
A Final Note
At Wonder, we build the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.
We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.
About Grubhub
At Grubhub, we believe food is more than just a meal: It’s a source of discovery, connection, and pure enjoyment. There’s a time and place for every type of dish, from hidden neighborhood gems to tried-and-true favorites, and we exist to connect people with the food they love in all the ways they like to dig in. We’ve been at it since 2004, but now, as part of Wonder, Grubhub is operating with a renewed sense of momentum and the high-velocity energy of a powerhouse startup.
As a leading U.S. ordering and delivery marketplace, we feature over 415,000 merchants in more than 4,000 cities, creating the ultimate food experience by elevating online ordering through innovative restaurant technology, easy-to-use platforms, and an improved delivery experience. We are constantly finding new ways to innovate—from integrated grocery delivery with groceries powered by Instacart to exclusive loyalty programs. Join our team, based out of New York City and Chicago, and help us give our diners the exceptional value they deserve.
About the Opportunity
At Wonder Data Science, our mission is to build data science and machine learning systems that improve how our marketplace operates, how customers experience the platform, and how the business makes high-quality decisions. As a Senior Staff Data Scientist, you will go beyond individual problem solving — you will help shape the strategic direction of applied data science, mentor senior and junior scientists, and collaborate closely with engineering, product, operations, and business leaders to move our ML and analytics capabilities toward scalable, production-grade systems.
You will identify high-leverage opportunities across the business, including marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing strategy, supply planning, demand forecasting, and operational performance. You will design statistically rigorous frameworks to understand causal impact, separate signal from noise, and guide business strategy through experimentation, measurement, and principled inference.
You will help define how we structure trade-offs like customer experience vs. operational efficiency, speed vs. cost, prediction accuracy vs. business impact, short-term metric movement vs. long-term marketplace health, and automation vs. human judgment. You’ll prototype, experiment, influence architecture, and ensure we operationalize models and insights that actually move business metrics — not just analyses that look good offline.
The Impact You Will Make
- Serve as a technical thought leader in Data Science — defining principles, frameworks, and best practices for how Wonder uses data, experimentation, and machine learning to improve customer, marketplace, and business outcomes.
- Mentor and coach a growing team of Data Scientists and contribute to career development and technical excellence across the group.
- Lead the exploration of interconnected marketplace systems, recognizing feedback loops between customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
- Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact.
- Partner with engineering to drive architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
- Define and implement robust experimentation strategies for changes that move business metrics in high-noise environments.
- Champion business-impact-driven data science, integrating causal inference, experimentation, risk-aware modeling, and scalable production ML systems that learn and adapt.
What You Bring to the Table
- 8+ years of industry experience with MS or 6+ years with PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field.
- Proven experience applying data science and machine learning to complex business problems, such as marketplace optimization, customer experience, forecasting, personalization, pricing, supply/demand balancing, operational policy changes, or product experimentation.
- Deep expertise in causal inference, experimentation, and statistical modeling, including methods such as A/B testing, difference-in-differences, regression discontinuity, instrumental variables, synthetic controls, uplift modeling, or causal impact analysis.
- Strong intuition for business and product trade-offs — customer experience vs. efficiency, ETA confidence vs. conversion risk, fulfillment reliability vs. cost, marketplace growth vs. quality, and short-term optimization vs. long-term health.
- Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design.
- Demonstrated ability to take data science, ML, or causal inference systems into production, partnering with engineering on architecture, deployment, and monitoring best practices.
- Fluency in SQL or similar tools for directly interrogating production-scale datasets.
- Experience mentoring and providing technical direction to other scientists, analysts, or engineers.
Got These? Even Better
- Experience leading end-to-end design of data science, machine learning, measurement, or experimentation frameworks within marketplace, consumer product, fulfillment, logistics, pricing, forecasting, or operations systems.
- Experience designing causal measurement strategies for complex systems where product, marketplace, and operational decisions interact across multiple layers.
- Background in causal inference, econometrics, Bayesian modeling, experimental design, or observational measurement in high-noise environments.
- Experience with applied experimentation frameworks, including A/B testing, power analysis, heterogeneous treatment effects, guardrail metrics, interference effects, and long-term impact measurement.
- Experience building or influencing production ML systems that combine predictive modeling, causal measurement, experimentation, and business rules.
- Influence across disciplines — able to align product, engineering, operations, business, and data science around a cohesive ML, experimentation, and measurement strategy.
- Experience defining strategy and technical roadmaps for data science, machine learning, experimentation, or causal inference platforms.
Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in-person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.
Location:
New York: $240,000 - $249,500 per year.
Illinois: $216,000 - $224,500 per year.
Wonder uses geographic-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.
Benefits
The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer a choice of medical, dental, and vision plans, company paid short and long term disability coverage, paid time off including flexible time off for exempt employees, paid vacation for non-exempt employees, and paid sick leave in compliance with applicable law in addition to paid parental leave, discounted meals and exclusive perks across the Wonder family of brands.
Eligibility, effective dates, and available plan options vary by employment classification and location. To learn more about benefits for this role, visit our Careers page here.
A Final Note
At Wonder, we build the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.
We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.
See all 299+ H-1B Visa Senior Staff Data Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Senior Staff Data Scientist Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Senior Staff Data Scientist
Map your role to the right SOC code
Senior Staff Data Scientist roles often straddle SOC 15-2051 (Data Scientists) and 15-1221 (Computer and Information Research Scientists). Pull the O*NET profile for each and match your actual job duties before your employer files the LCA. Misclassification is a leading RFE trigger at this level.
Document your prevailing wage tier early
Run an OFLC Wage Search using your work location's metropolitan statistical area before negotiating an offer. At the Senior Staff level, you're likely in Wage Level III or IV. Confirming this before the LCA is filed prevents last-minute offer renegotiation or refilings.
Target employers with cap-exempt filing history
Universities, nonprofit research organizations, and certain government-affiliated labs are H-1B cap-exempt, meaning they can file year-round without lottery exposure. Senior Staff Data Scientists in applied research or faculty-adjacent roles are often eligible. Filtering by employer type narrows your search to organizations that can move fast.
Find sponsors through verified LCA filing data on Migrate Mate
Search Senior Staff Data Scientist roles on Migrate Mate to see which employers have active LCA filing history for this exact title. That filters out companies that say they sponsor but rarely do, so you're applying where H-1B petitions for this level actually get filed.
Prepare a specialty occupation memo before interviews
At the Senior Staff level, USCIS scrutinizes whether the role genuinely requires a specific bachelor's degree field, not just any advanced degree. Draft a one-page summary linking your degree discipline to the technical scope of the role before offer stage. Employers appreciate candidates who make the petition easier to defend.
Negotiate premium processing into your offer
USCIS premium processing moves I-129 adjudication to 15 business days. For Senior Staff roles with delayed start dates or competitive counteroffers in play, request that your employer elect premium processing as a condition of acceptance. Most large tech and finance sponsors treat this as standard for senior hires.
H-1B Visa Senior Staff Data Scientist: Frequently Asked Questions
Does a Senior Staff Data Scientist role qualify as a specialty occupation for H-1B purposes?
Yes, in nearly all cases. USCIS evaluates whether the position normally requires at least a bachelor's degree in a specific field. Senior Staff Data Scientist roles consistently meet this bar because they require specialized training in statistics, computer science, or a closely related discipline. Staff and principal-level positions actually carry less specialty occupation risk than entry-level roles, since the technical depth is easier to document in the LCA and support letter.
Which employers sponsor H-1B visas for Senior Staff Data Scientists?
Technology companies, financial institutions, healthcare analytics firms, and research-focused organizations are the most active sponsors for this title. Because the role sits at a senior individual contributor level, sponsors tend to be organizations with established immigration programs rather than early-stage startups. Migrate Mate surfaces employers with verified LCA filing history for Senior Staff Data Scientist roles specifically, so you can identify active sponsors before applying.
How does the H-1B lottery affect Senior Staff Data Scientist hiring timelines?
The annual H-1B lottery runs in March for an October 1 start date, creating a roughly six-month gap between selection and work authorization. Senior Staff roles are often critical enough that employers use this window to negotiate a delayed start or bridge the gap with consulting arrangements. Cap-exempt employers, such as universities and nonprofit research labs, can file outside the lottery entirely, which is worth factoring into your job search at this level.
What wage level should a Senior Staff Data Scientist expect on the LCA?
Most Senior Staff Data Scientist roles are classified at DOL Wage Level III or IV, reflecting the advanced knowledge, independent judgment, and scope of responsibility the title implies. Your employer must certify on the LCA that your offered wage meets or exceeds the prevailing wage for your SOC code in your work location. You can verify the applicable wage floor using the OFLC Wage Search before finalizing your offer.
Can a Senior Staff Data Scientist switch employers while on H-1B?
Yes, H-1B portability under AC21 lets you start work with a new employer as soon as your new employer files a concurrent or new H-1B petition, provided your current H-1B has been approved for at least 180 days. For Senior Staff roles, the specialty occupation documentation from your previous petition can inform the new filing, but your new employer needs to file their own I-129. Make sure you don't let your current status lapse before the new petition is received by USCIS.