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About This Role
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, Chicago and Denver, 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
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- 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.
\#LI\-Hybrid
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
We offer a competitive salary package including equity and 401K. Additionally, we provide multiple medical, dental, and vision plans to meet all of our employees' needs as well as many benefits and perks that are not listed.
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.
Salary Context
This $216K-$249K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Wonder, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($232K) sits 18% above the category median. Disclosed range: $216K to $249K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Wonder AI Hiring
Wonder has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $191K - $249K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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