ZipRecruiter

Log In

1

Causal Inference Jobs in Chicago, IL (NOW HIRING)

Novus Print Media

Director, Data Science

Chicago, IL · Hybrid

Custom causal inference designs -- incrementality, geo holdouts, synthetic controls * Audience, channel, and creative mix analyses, and productionized tools that support the broader Analytics team ...

NOVUS

Director, Data Science

Chicago, IL · On-site

Custom causal inference designs - incrementality, geo holdouts, synthetic controls * Audience, channel, and creative mix analyses, and productionized tools that support the broader Analytics team ...

Uber Freight

Applied Scientist III

Chicago, IL · On-site +1

Develop creative solutions and build prototypes to business problems using algorithms based on causal inference, statistics, machine learning, and optimization. * Collaborate with engineering and ...

Uber Freight

Applied Scientist III

Chicago, IL · On-site

Develop creative solutions and build prototypes to business problems using algorithms based on causal inference, statistics, machine learning, and optimization. * Collaborate with engineering and ...

next page

Showing results 1-20

People also search for

All JobsCausal Inference JobsCausal Inference Jobs in Chicago, IL

Causal Inference information

See Chicago, IL salary details

$56.7K

$102.2K

$139.6K

How much do causal inference jobs pay per year?

As of May 29, 2026, the average yearly pay for causal inference in Chicago, IL is $102,222.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,600.00 and $111,800.00 per year, depending on experience, location, and employer.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.
More about Causal Inference jobs
What are the most commonly searched types of Causal Inference jobs in Chicago, IL? The most popular types of Causal Inference jobs in Chicago, IL are:
What are popular job titles related to Causal Inference jobs in Chicago, IL? For Causal Inference jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Chicago, IL look for? The top searched job categories for Causal Inference jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Causal Inference jobs? Cities near Chicago, IL with the most Causal Inference job openings:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Chicago, IL as of May 2026, with employment types broken down into 1% Internship, 94% Full Time, 4% Part Time, and 1% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $102,222 per year, or $49.1 per hour.
Applied Machine Learning Engineer

Applied Machine Learning Engineer

Strata Decision Technology

Chicago, IL • On-site

$117K - $150K/yr

Other

Medical, Life, Retirement, PTO

Posted 6 days ago

Job description

How You'll Make an Impact
As an Applied Machine Learning Engineer, you will collaborate with architects, data scientists, agentic AI developers, platform engineers, and the product team to build advanced AI and ML capabilities into our platform. Your work will drive innovation in generative AI and beyond, integrating and customizing a wide range of machine learning techniques to solve complex problems in healthcare. By developing next-generation AI agents, algorithms, and computation engines, you will help Strata strengthen its market leadership, improve operational efficiency, and support healthcare providers in delivering high-quality care while maintaining financial health.

A Day in the Life

  • Read and translate the latest research (e.g., arXiv papers) into production-ready solutions in Python.

  • Prototype and iterate on machine learning models, focusing on areas such as regression, causal inference, optimization, and vector embeddings.

  • Collaborate with cross-functional teams to embed ML and AI capabilities directly into our software platform.

  • Partner with data scientists to design experiments and apply statistical concepts to real-world data.

  • Optimize, test, and scale ML models to support mission-critical healthcare analytics.

Our Technology Stack
Our core platform is used by more than half of the nation's leading healthcare providers, enabling them to leverage financial, operational, and clinical data. Our AI and ML stack includes:

  • Languages & Libraries: Python, PyTorch, NumPy, Pandas, Polars, PyMC

  • Infrastructure: AWS, Snowflake, Docker, GitHub

  • Techniques & Tools:

    • Regression (with and without Bayesian priors)

    • Vector embeddings, similarity, clustering

    • Core statistics and distributions for EDA

    • Optimization methods (multi-armed bandit, mixed integer programming)

    • Causal inference and probabilistic modeling

What We're Looking For
We're seeking a technically curious engineer who thrives on turning theory into practice. The ideal candidate has:

  • Strong experience implementing ML models in Python.

  • Familiarity with regression, embeddings, causal inference, and optimization techniques.

  • Experience applying statistical methods to exploratory data analysis.

  • Comfort working with modern ML libraries and frameworks.

Bonus points if you have worked with:

  • NLP tasks (LLMs, spaCy, neural networks).

  • Recommender systems, latent factors, matrix factorization.

  • Graph algorithms.

  • Claude Code, Docker, and GitHub.ess computation engine.

Estimated Salary Range: $117,000-150,000
Actual salary will be determined based on factors including, but not limited to, skill set and level of experience. This salary range is a good faith estimate of base pay. Strata also provides discretionary variable pay programs based on role. In addition, Strata provides a comprehensive benefits package including retirement benefits, health and welfare benefits, paid time off, parental leave, life and accident insurance, and other voluntary and well-being benefits.

Apply