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Causal Inference Machine Learning Postdoctoral Jobs in Chicago, IL

Grubhub

Senior Staff Data Scientist

Chicago, IL · On-site

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 ...

Quantum Machines

Machine Learning Engineer

Chicago, IL · On-site

Description Quantum Machines (QM) is a global leader in quantum computing control systems. Through ... You will develop reinforcement learning policies, Bayesian inference methods, and agentic ...

IMC

Hardware Machine Learning Engineer

Chicago, IL

$127K - $167K/yr

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Chicago, IL

Causal Inference Machine Learning Postdoctoral information

See Chicago, IL salary details

$36.6K

$55.9K

$62.8K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 14, 2026, the average yearly pay for causal inference machine learning postdoctoral in Chicago, IL is $55,858.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,100.00 and $58,200.00 per year, depending on experience, location, and employer.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL? For Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Chicago, IL with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you

Senior Staff Data Scientist

Grubhub Holdings Inc.

Chicago, IL

Full-time

Re-posted 12 days ago

Job description

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  

  • 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.Â