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Data Science Machine Learning Jobs in Chicago, IL

Master's degree preferred * 3 to 5 plus years of experience in data science, machine learning, or advanced analytics roles in a product or business-driven environment. * Demonstrated experience ...

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 ...

Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine Learning, Applied AI, Statistics, Quantitative Analytics, or Data Analytics. * Experience producing or ...

Posted today

Use the most effective machine learning techniques to answer data science questions, define data requirements for model development, develop models, and evaluate model performance * Document projects ...

Use the most effective machine learning techniques to answer data science questions, define data requirements for model development, develop models, and evaluate model performance * Document projects ...

Data Scientist

Rosemont, IL · On-site

$99K - $124K/yr

Use the most effective machine learning techniques to answer data science questions, define data requirements for model development, develop models, and evaluate model performance * Document projects ...

Showing results 21-40

Data Science Machine Learning information

See Chicago, IL salary details

$38.7K

$126.5K

$202.6K

How much do data science machine learning jobs pay per year?

As of Aug 12, 2026, the average yearly pay for data science machine learning in Chicago, IL is $126,534.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $140,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a data science machine learning professional?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What are some common challenges faced when deploying machine learning models as a data science machine learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.
Infographic showing various Data Science Machine Learning job openings in Chicago, IL as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $126,534 per year, or $60.8 per hour.

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 6 days ago


AAR Corp rating

7.8

Company rating: 7.8 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

22nd of 65 rated aviation services


Job description

The AAR Parts Supply Distribution Analytics Team are developing an AI-driven aviation market intelligence tool designed to transform how aviation parts distribution businesses understand market share, identify growth opportunities, and make strategic decisions. This platform combines advanced analytics, machine learning, agentic AI, and intuitive user experience to provide real-time insights into customers, parts, and markets. 

The Data Scientist will define, build, and continuously improve the analytical and machine learning models that power the platforms core decision-making capabilities. This role translates complex business problems into scalable, production-ready models that drive market sizing, forecasting, and opportunity identification. The Data Scientist partners closely with product, engineering, and data teams to ensure models are accurate, explainable, and embedded into real-world workflows. Success in this role requires strong technical depth, business intuition, and the ability to operate in ambiguous, data-rich environments. 

This position is based at our Corporate Headquarters in Wood Dale, IL, with a planned relocation to the Merchandise Mart (Chicago) in early 2027.

What you will be responsible for: 

  • Design, develop, and own scalable analytical and machine learning models for market sizing, forecasting, opportunity identification, and optimization use cases. 
  • Translate ambiguous business problems into structured modeling approaches, including feature engineering, model selection, and evaluation frameworks. 
  • Design and analyze experiments, statistical tests, and validation methods to measure model quality and business impact. 
  • Deploy and integrate models into production systems in collaboration with data engineering and backend teams, ensuring reliability, scalability, and performance. 
  • Work with large, complex, and imperfect datasets; define data requirements and support robust preprocessing and feature pipelines. 
  • Ensure model outputs are explainable, interpretable, and aligned with business logic to support user trust and adoption. 
  • Monitor, validate, and improve model performance through testing, retraining, versioning, and feedback loops. 
  • Partners with product teams to define analytical features, influence roadmap decisions, and embed model-driven insights into decision-making workflows. 

What you need to be successful in this role: 

  • Strong foundation in statistics, machine learning, and predictive modeling, including regression, classification, clustering, time series, and experiment design e.g., AB testing. 
  • Proficiency in Python and SQL, with experience using common ML libraries such as scikit-learn, pandas, and numpy. 
  • Experience building end-to-end ML workflows, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. 
  • Familiarity with production ML practices, including model versioning, performance optimization, retraining, and monitoring for drift or degradation. 
  • Experience working with large-scale or complex datasets, including handling missing data, inconsistencies, and real-world data limitations. 
  • Ability to communicate model logic, assumptions, and outputs clearly to non-technical stakeholders and cross-functional partners. 
  • Experience collaborating with product managers, data engineers, and software engineers to deliver analytical features in production environments. 
  • Strong problem-solving skills and the ability to operate effectively in ambiguous environments with evolving requirements. 
  • Bachelors in data science, Statistics, Mathematics, Engineering, Computer Science, or a related quantitative field. 
  • Master's degree preferred 
  • 3 to 5 plus years of experience in data science, machine learning, or advanced analytics roles in a product or business-driven environment. 
  • Demonstrated experience developing, deploying, and improving predictive models that drive business decision-making. 
  • Experience working on analytics or ML-driven products such as forecasting, optimization, recommendation systems, or market intelligence tools.
  • Experience in designing experiments or statistical validation approaches to evaluate model performance and business outcomes is preferred. 
  • Experience with cloud platforms or distributed data processing such as AWS, Azure, or Spark is preferred but not required. 
  • Experience in aviation, supply chain, or related industries is a plus but not required. 

The rewards of your career at AAR go far beyond just your salary: 

  • Competitive salary and bonus package
  • Comprehensive benefits package including medical, dental, and vision coverage.
  • 401(k) retirement plan with company match
  • Generous paid time off program

  •  Professional development and career advancement opportunities

Physical Demands/Work Environment:
The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
    Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
    While performing the duties of this job, the employee may be regularly required to sit, stand, bend, reach and move about the facility.
    The environmental characteristic for this position is an office setting.
    Candidates should be able to adapt to a traditional business environment.
AAR provides accommodation in accordance with applicable laws through all stages of the hiring process. If you require accommodation for any part of the application and/or hiring process, please advise Human Resources.

Compensation:
The anticipated salary range for this position is $115,000 to $130,000 annually. This range reflects the base salary for candidates who meet the requirements of the role, including experience, education, and location. In addition to base pay, this role is eligible for a bonus. AAR offers a competitive benefits package, including medical/dental/vision/life/and AD&D insurance, 401(k) savings plan with employer match, paid time off and holiday pay, as well as opportunities for professional development and growth.

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