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Scientific Machine Learning Jobs in Illinois (NOW HIRING)

Gotion, Inc.

Machine Learning Engineer

Manteno, IL

Collaborate with battery scientists and domain experts to incorporate physical constraints or ... D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied ...

Socket.dev

Machine Learning Engineer

Oakbrook Terrace, IL · On-site

$110 - $145/hr

We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional ... Partner closely with Data Scientists to support traditional ML model development, including feature ...

New

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field * Demonstrated expertise in model development, optimization, and algorithmic ...

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 ...

Apex Systems

Senior Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

Hyatt seeks an extraordinary Machine Learning Engineer to help build the algorithmic assets and ... Partner with data scientists to develop prototype solutions of algorithmic products leveraging ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Illinois?

For Scientific Machine Learning jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Illinois look for?

The top searched job categories for Scientific Machine Learning jobs in Illinois are:

What cities in Illinois are hiring for Scientific Machine Learning jobs?

Cities in Illinois with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Illinois as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Data Scientist - Machine Learning & AI

Team Velocity Marketing

Virginia, IL • On-site

$160 - $190/hr

Other

Medical, Dental, Vision, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

Senior Data Scientist – Machine Learning & AI

Senior Data Scientist – Machine Learning & AI
Remote | Full-Time | $160,000–$190,000

Team Velocity is seeking a Senior Data Scientist to develop and deploy machine learning, predictive analytics, and AI solutions that improve customer engagement, marketing performance, operational efficiency, and business intelligence.

This is a hands‑on role for an experienced data scientist who can take models from data exploration and development through production deployment, monitoring, and optimization. You will partner with Product, Data Engineering, Software Engineering, Analytics, and business leadership to deliver measurable business impact.

This is a full‑time remote position. Candidates must reside in the Continental U.S. and be able to support an 8:30 AM–5:30 PM ET business hours. Eastern and Central Time Zones highly preferred.

KEY RESPONSIBILITIES
  • Design, build, evaluate, and deploy production machine learning models.
  • Develop predictive models for churn, propensity, lead scoring, customer lifetime value, recommendations, forecasting, personalization, and marketing attribution.
  • Perform statistical analysis, hypothesis testing, A/B testing, causal inference, and time-series analysis.
  • Build feature engineering, model training, and inference pipelines.
  • Deploy and monitor ML models, including model performance, drift detection, and retraining.
  • Apply Generative AI, LLMs, RAG, and vector databases to business and customer applications.
  • Partner with Product, Engineering, Analytics, and leadership to translate business problems into scalable data science solutions.
  • Mentor junior data scientists and establish best practices for model development, documentation, and code quality.
REQUIREMENTS
  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field; Master's or PhD preferred.
  • 5+ years | Python + SQL | production ML | predictive modeling | model deployment | MLOps | cloud | measurable business impact
  • Proven ability to deliver measurable business impact through data science and machine learning.
  • Strong communication, analytical, and business problem‑solving skills.
  • Expert Python and SQL skills.
TECHNICAL EXPERIENCE
  • Statistics: Regression, Bayesian methods, hypothesis testing, experimental design, causal inference, time series
  • Data & Cloud: Snowflake, dbt, Spark, Airflow, GCP preferred; AWS or Azure considered
  • AI/LLMs: OpenAI, Gemini, Claude, LangChain, LangGraph, RAG, embeddings, vector databases
  • Experience with data quality and observability tools such as Great Expectations or Monte Carlo is a plus.

*You do not need experience with every technology listed above. Strong production machine learning experience is the priority.

Preferred Experience
  • Large‑scale customer or behavioral data
  • Marketing analytics, personalization, or customer intelligence
  • SaaS, automotive, retail, advertising, or marketing technology
  • Real‑time inference or streaming data
  • Production Generative AI applications
COMPENSATION & BENEFITS

The expected salary range is $160,000–$190,000 annually, based on experience, skills, and qualifications. Benefits include medical, dental, vision, 401(k) matching, unlimited paid leave, wellness programs, and more.

About Team Velocity

Team Velocity is a full‑service marketing and technology company serving automotive manufacturers and dealerships nationwide. Our proprietary Apollo® technology platform uses data, predictive analytics, and AI to predict consumer behavior, personalize marketing, and help dealerships increase sales and service revenue.

Join us in applying data science, machine learning, and AI to real‑world business problems at scale.

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