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Data Science Machine Learning Jobs in Illinois (NOW HIRING)

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems. * Apply advanced statistical, mathematical, and analytical techniques to model ...

Data Scientist

Bolingbrook, IL · On-site

$102K - $130K/yr

This team utilizes the power of data science and machine learning techniques to address the diverse business questions and challenges facing Ulta Beauty. As a Data Scientist, you will succeed by ...

Data Scientist

Bolingbrook, IL · On-site

$102K - $130K/yr

This team utilizes the power of data science and machine learning techniques to address the diverse business questions and challenges facing Ulta Beauty. As a Data Scientist, you will succeed by ...

Data Scientist

Wood Dale, IL · On-site

$115 - $130/hr

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 ...

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 ...

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 ...

Showing results 21-40

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 Sep 2, 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.

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.

What cities in Illinois are hiring for Data Science Machine Learning jobs?

Cities in Illinois with the most Data Science Machine Learning job openings:

Infographic showing various Data Science Machine Learning job openings in Illinois as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $118,937 per year, or $57.2 per hour.

Full-time

Posted 14 days ago


Job description

We are seeking a Data Scientist with strong experience in advanced analytics, statistical modeling, machine learning, and artificial intelligence. The ideal candidate will be responsible for analyzing complex structured and unstructured data, developing actionable insights, building data science solutions, and partnering with data and business teams to solve complex problems and deliver measurable business value.

Primary Responsibilities
  • Analyze large and complex structured and unstructured datasets from multiple internal and external sources to identify trends, patterns, correlations, and actionable insights.

  • Design, develop, and implement data science, machine learning, and AI solutions to address complex business problems.

  • Apply advanced statistical, mathematical, and analytical techniques to model data and support data-driven decision-making.

  • Perform exploratory data analysis (EDA) and develop high-quality datasets for statistical analysis and machine learning.

  • Integrate data from multiple sources and develop processes to consolidate, transform, and prepare data for analysis.

  • Design and define data structures and storage approaches for unstructured and diverse datasets, including how data is consumed, integrated, and managed.

  • Develop analytical models and algorithms to understand relationships and correlations across disparate datasets.

  • Identify data quality issues and provide guidance on data transformation, cleansing, and preparation for analytical use.

  • Generate reports, dashboards, datasets, and other analytical resources to communicate insights to business and technical stakeholders.

  • Translate complex business requirements and technical designs into data science and AI solutions aligned with organizational goals.

  • Collaborate closely with Data Analysts, Data Engineers, Business Stakeholders, and other technical teams to understand requirements and deliver effective solutions.

  • Review data pipelines and provide recommendations for data quality, performance, scalability, and optimization.

  • Develop reusable analytical processes, reporting solutions, and data products to support ongoing business needs.

  • Communicate analytical findings and recommendations clearly to both technical and non-technical stakeholders.

  • Provide guidance and coaching on data preparation, analytical techniques, and best practices when needed.

Required Qualifications & Experience
  • 5+ years of experience in Data Science, Advanced Analytics, Machine Learning, or a related field.

  • Strong experience working with large, complex, structured, and unstructured datasets.

  • Strong knowledge of statistics, mathematics, data analysis, and predictive modeling.

  • Hands-on experience with machine learning and AI techniques.

  • Strong proficiency in SQL and experience working with databases or data warehouses.

  • Experience with Python, R, or similar programming languages for data analysis and modeling.

  • Experience performing exploratory data analysis, data preparation, transformation, and cleansing.

  • Experience integrating and analyzing data from multiple sources.

  • Strong understanding of data pipelines, data structures, and data engineering concepts.

  • Experience developing analytical models and translating business requirements into technical data science solutions.

  • Strong problem-solving and analytical skills, with the ability to investigate complex data relationships and identify meaningful insights.

  • Excellent communication skills with the ability to present complex analytical findings to both technical and non-technical audiences.

  • Experience collaborating with cross-functional teams, including Data Engineering, Data Analytics, Product, and Business teams.