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Data Science Machine Learning Jobs in Chicago, IL

YO AI Labs

AI Data Scientist Expert - Remote

Chicago, IL · Remote

$100 - $200/hr

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 ...

Vizient

AVP, Machine Learning & Modeling

Chicago, IL · On-site

$156K - $290K/yr

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Reyes Holdings

Data Scientist

Rosemont, IL · On-site

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 ...

Reyes Holdings

Data Scientist

Rosemont, IL · On-site

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 ...

Reyes Holdings

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 ...

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Showing results 21-40

All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Chicago, IL

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 Sep 2, 2026, the average yearly pay for data science machine learning in Chicago, IL is $126,538.00, 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 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.

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.

Is data science machine learning a high paying job?

Data science and machine learning roles are generally high-paying within the tech industry due to the specialized skills required, such as programming, statistical analysis, and experience with tools like Python or TensorFlow. Salaries vary based on experience, location, and company size but tend to be above average compared to many other professions.
Data Science Machine Learning jobs near you
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, 80% Full Time, 15% Part Time, 3% Contract, and 1% Nights. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $126,438 per year, or $60.8 per hour.

Director, Data Engineering & Data Science Engineering

Ritchie Bros.

Westchester, IL • On-site

Full-time

Re-posted 6 days ago

Ritchie Bros. Auctioneers rating

7.5

Company rating: 7.5 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

5th of 19 rated auctioneers

Job description

About the Role

IAA is seeking a Director of Data Engineering & Data Science Engineering to lead a highly visible, business-critical function at the intersection of data, analytics, machine learning, and business transformation. This leader will define and drive the vision, architecture, and execution for IAA's data engineering and data science capabilities, ensuring the organization can scale advanced analytics, BI, forecasting, machine learning, and AI solutions that directly support business growth and operational excellence.

This role requires a strong technical leader and business problem solver who can partner across a broad set of stakeholders including Operations, Business, Sales, Marketing, Product, and Engineering. The ideal candidate brings deep expertise in the Azure BI and data ecosystem, strong people leadership, and the ability to translate complex business needs into practical, scalable data and AI solutions.

This position reports directly to the VP of Engineering and is a critical, high-visibility leadership role within the organization.

What You'll Do

  • Lead the Data Engineering and Data Science Engineering function for IAA, setting technical vision, delivery strategy, and operating rhythm
  • Build and evolve scalable data platforms, BI architecture, and ML-enablement capabilities using the Azure data and analytics stack
  • Drive strategy and execution across Microsoft Fabric, Synapse, Power BI, Azure BI technologies, and modern cloud data platforms
  • Partner with business and functional leaders to solve high-value problems across Operations, Sales, Marketing, Product, and other key areas
  • Guide the design and implementation of robust pipelines, semantic models, dashboards, self-service analytics, forecasting solutions, and machine learning systems
  • Help shape the roadmap for advanced analytics, predictive modeling, experimentation, and AI-driven insights
  • Mentor, coach, and grow data engineering and data science talent while raising the technical bar across the team
  • Establish strong engineering practices across architecture, delivery quality, scalability, governance, and operational excellence
  • Collaborate closely with engineering leaders and cross-functional teams to ensure data and AI solutions are aligned with platform, product, and business priorities
  • Act as a senior thought partner to leadership on data strategy, technical tradeoffs, and investment priorities

What We're Looking For

  • Proven experience leading Data Engineering, BI, Analytics, and/or Data Science Engineering teams at the Director level or equivalent
  • Deep expertise in the Azure BI / data technology stack, including:
    • Microsoft Fabric
    • Azure Synapse Analytics
    • Power BI
    • Broader Azure data and analytics services
  • Strong understanding of data engineering architecture, modern analytics platforms, and scalable data pipelines
  • Strong foundation in data science, machine learning, and model operationalization
  • Demonstrated ability to solve complex business problems through data, analytics, and technical leadership
  • Strong mentoring, coaching, and people leadership skills with experience growing high-performing technical teams
  • Excellent communication and stakeholder management skills; able to work effectively with a wide range of technical and non-technical partners such as Ops, Business, Sales, Marketing, Product, Engineering 
  • Ability to operate successfully in a fast-paced, high-visibility environment with multiple priorities and stakeholders
  • Strong executive presence and the ability to connect technical decisions to business outcomes

Preferred Experience

  • Experience supporting enterprise use cases across operations, commercial functions, and product-driven organizations
  • Experience driving both BI modernization and data science / ML adoption within the same organization
  • Familiarity with cloud-native engineering practices, production-grade data platforms, and secure, scalable AI/ML environments
  • Experience leading organizations that combine data engineering, analytics engineering, BI, and data scienceunder one leadership model

IAA Data Science / Engineering Technology Environment

We are looking for a leader who can guide and expand a modern data and AI ecosystem. Relevant technologies include Azure BI capabilities as well as IAA's broader data science and ML toolset, including technologies such as Python, SQL, Azure Event Hub, Apache Airflow, Synapse, Fabric, Docker, Terraform, DBT, PyTorch, TensorFlow, Vertex AI, Gemini, GPT, Prophet, TBATS, SARIMAX, scikit-learn, CI/CD pipelines, and Azure cloud platform.

Why This Role Matters

This is a critical leadership role for IAA. The Director will help shape how the company uses data, analytics, BI, and AI to make better decisions, improve business performance, unlock operational efficiencies, and create scalable competitive advantage. This leader will influence both technical direction and business outcomes across the organization.

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