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Data Science Machine Learning Jobs in Illinois (NOW HIRING)

SynergisticIT

Junior Data Engineer

Evanston, IL · On-site

Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers for full time positions ...

Darwill, Inc.

Machine Learning Engineer

Villa Park, IL · On-site

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized ... Partner closely with Data Scientists to support traditional ML model development, including feature ...

Paylocity

Machine Learning Engineer

Schaumburg, IL · On-site

We develop machine learning models and infrastructure to support internal team strategies and collaborate closely with our data science organization to drive efficiency and best practices. Your ...

Addison Group

Sr. Data Scientist

Chicago, IL · On-site

$85 - $100/hr

... data science projects, ensuring alignment with business goals, scope, and defined KPIs. • Design, implement, and optimize advanced machine learning and optimization models to address complex ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Illinois

Data Science Machine Learning information

See Illinois salary details

$36.3K

$118.9K

$190.4K

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

As of Jun 29, 2026, the average yearly pay for data science machine learning in Illinois is $118,937.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,400.00 and $131,800.00 per year, depending on experience, location, and employer.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

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 engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

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.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

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.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
What cities in Illinois are hiring for Data Science Machine Learning jobs? Cities in Illinois with the most Data Science Machine Learning job openings:
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Illinois as of June 2026, with employment types broken down into 59% Full Time, 37% Part Time, 2% Contract, and 2% Nights. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $118,937 per year, or $57.2 per hour.
Director of Data Engineering & Data Science - IAA

Director of Data Engineering & Data Science - IAA

The Job Sauce

Chicago, IL • On-site

Full-time

Posted 27 days ago

Job description

About the Role
IAA is seeking a Director of Data Engineering & Data Science 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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