Top Machine Learning Jobs in Chicago, IL

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Lead and coach a data science team to design, develop, and deploy predictive and prescriptive analytics. Partner with business leaders to identify opportunities, drive adoption of models and insights, manage vendors, and establish data science methodology to deliver measurable business impact.

Lead design and delivery of AI/GenAI solutions and scalable data infrastructure. Drive business development, manage multiple client engagements, mentor teams, implement AI models using Python and C++, ensure data quality, and promote innovation across projects.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

The Director, AI Solutions Architect leads the development of scalable AI solutions, ensuring quality and compliance while collaborating with cross-functional teams.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.

Build and maintain pricing and profitability models to optimize lending strategies, incorporating proprietary credit models. Design A/B tests, analyze financial performance trends, and present data-driven recommendations to senior management. Collaborate with credit risk, capital markets, FP&A, marketing, and sales to quantify financial impact and improve pricing decisions.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead development and implementation of advanced AI/ML solutions, design and optimize models and algorithms, transform raw data into actionable insights, manage projects end-to-end, supervise and mentor teams, maintain quality and standards, and manage client engagements and expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

This internship focuses on researching low-latency ML inference and hardware acceleration, involving project ownership, collaboration with engineers, and presenting findings.

Architect and lead development of scalable AI and cloud-native agentic solutions and AI transcription platforms using OpenAI, Azure, and AWS. Design, build, and maintain high-performance distributed systems; provide technical leadership, enforce engineering best practices, lead DevOps (Docker, Kubernetes, AWS Fargate) and CI/CD with SonarQube, optimize infrastructure costs, and collaborate cross-functionally to align technical solutions with financial business needs.

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

The Senior/Staff Data Scientist will build and deploy predictive models for consumer decisioning and business optimization, focusing on machine learning and statistical modeling. Responsibilities include model development lifecycle, data exploration, collaboration with teams, and improving product outcomes.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Lead design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

Lead and scale a cross-functional AI and engineering organization that builds production-grade AI models, automated data pipelines, and LLM-driven workflows to power Samsara's GTM operations. Define a multi-year AI roadmap, set quality and review standards for model outputs, collaborate with execs across Engineering, Product, and RevOps, and hire and mentor managers and individual contributors to drive adoption and reliable automation across CRM, telemetry, and enterprise data systems.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

The VP of Agentic Systems will lead data science research and development, focusing on AI systems to enhance cybersecurity operations, requiring strong leadership and collaboration across technical teams.