Top Machine Learning Jobs in Chicago, IL

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Develop and deploy AI solutions, including machine learning models, generative AI applications, agents, and data pipelines. Partner with business teams to identify automation and decision-support opportunities, monitor production performance, optimize solutions using feedback, and establish reusable tools, development standards, and best practices. The role requires Python and SQL proficiency, hands-on AI experience, and strong communication skills.

Uses advanced mathematical and statistical methods, machine learning, and probabilistic modeling to solve business problems. Collects, integrates, and analyzes healthcare and business data; validates trends; develops and evaluates predictive algorithms; conducts experiments; and creates visualizations and analytical applications. The role also involves designing, deploying, and evaluating generative AI and agentic AI solutions, including LLM applications, retrieval, tool use, orchestration, observability, and human review.

Leads personal loan acquisition data science, developing and deploying machine learning models for customer targeting, campaign optimization, underwriting, pricing, and verification. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using Python, Spark, AWS, and related technologies; validates and backtests models; translates technical findings into business outcomes; and develops team talent.

Develop and productionize statistical, predictive, optimization, machine learning, and AI solutions. Own data science projects end to end, including data exploration, modeling, validation, deployment, monitoring, and adoption. Analyze data quality, create dashboards and data products, communicate findings, collaborate with engineering and business teams, follow governance standards, mentor colleagues, and improve analytics practices.

Designs and builds large-scale clinical, claims, and document data platforms on Google Cloud. Leads architecture, production engineering, AI-assisted development and operations, including agent guardrails, observability, automation, auditability, and regulated-environment controls. Develops AI and machine learning systems involving retrieval, extraction, evaluation, and serving. Partners across engineering, security, privacy, compliance, and governance teams while communicating technical strategies to executives and customers.

Designs and leads enterprise AI transformations for complex Workday customers across HR, Finance, and IT. Advises executive stakeholders, defines AI-enabled processes and operating models, leads discovery and design workshops, guides configuration and data readiness, and supports activation and adoption. Partners with Product, Engineering, integration, and change-management teams to deliver ethical, secure, scalable AI solutions tied to measurable business outcomes. Uses customer feedback to improve deployment methods, templates, and best practices.

Owns the vision, roadmap, governance, lifecycle, and adoption of AI/ML technology platforms. Translates stakeholder needs into platform requirements, prioritizes capabilities, coordinates engineering dependencies, and improves reliability, security, cost, and developer experience. Partners with engineering and product teams on integrations, standards, agile delivery, incident communications, operational improvements, and value measurement. Requires extensive platform or technical leadership experience, AI/ML expertise, Azure, and Databricks experience.

Leads data science and model risk oversight for credit card underwriting models. Builds, evaluates, validates, monitors, and implements machine learning models using large-scale structured and textual data. Partners with data scientists, engineers, and product managers to translate complex statistical findings into business outcomes, ensure effective model governance, and identify emerging technologies and analytical methods.

Leads data scientists, business analysts, and data analysts in developing machine learning models from design through implementation. Partners with software engineering and product teams to deliver customer-focused products, using Python, cloud computing, Spark, H2O, and other technologies to analyze large-scale numeric and textual data. Translates complex technical findings into business goals while developing team talent and evaluating emerging methods.

Leads data science for foundational AI and automation across a global payments network. Partners with data scientists, engineers, ML engineers, and product managers to develop scalable models and AI agents using PyTorch, AWS, vector databases, and LLMs. Serves as an NLP and LLM domain expert, translates technical work into business value, develops talent, and delivers production-scale libraries, platforms, and solutions.

Leads the strategy, design, and deployment of AI and machine learning solutions for tax-focused client engagements. Builds Python-based systems for text analytics, extraction, and sentiment analysis; oversees data preparation, feature engineering, model tuning, and platform integration. The Director develops client relationships, drives business development, mentors teams, promotes responsible AI adoption, and ensures governance and professional standards across initiatives.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Leads engineering teams delivering enterprise AI, intelligent automation, digital capture, and document intelligence platforms. Owns technology strategy, architecture, modernization, operational excellence, governance, reliability, security, and compliance for mission-critical systems. Develops engineering organizations through hiring, coaching, succession planning, and performance management while partnering with product, architecture, operations, business, and executive stakeholders. Drives adoption of generative AI, agentic automation, workflow orchestration, cloud-native architecture, and modern delivery practices to achieve measurable business outcomes.

Leads end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

Leads the design, development, deployment, and automation of production-grade machine learning and optimization solutions. Builds scalable data and ETL pipelines, internal ML and ERP tools, and reusable engineering frameworks. Ensures reliability through software engineering and DevOps practices, evaluates emerging technologies, documents architectures, collaborates cross-functionally, and mentors team members. Work includes AWS, Databricks, optimization modeling, web applications, databases, orchestration, containers, and machine learning frameworks.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant intelligence, and monitoring. Partners with data scientists, engineers, and product managers to build scalable models and AI tools. Responsibilities span model design, training, evaluation, validation, and implementation using Python, AWS, H2O, Spark, and other technologies. The role also requires stakeholder communication, innovation, and team talent development.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Leads enterprise AI strategy, roadmaps, and project portfolios from ideation through deployment. Manages AI scientists and engineers, partners with executives and stakeholders, translates business needs into technical requirements, and drives AI adoption, governance, education, and measurable business impact. Oversees solutions using analytics, machine learning, generative AI, data engineering, and related technologies while establishing success metrics and ensuring timely, budget-conscious delivery.

Builds firmwide AI infrastructure and production-grade generative AI applications, including agentic workflows using RAG, semantic search, planning, and structured outputs. The role develops evaluation harnesses, guardrails, approval workflows, tool-use controls, source attribution, and kill switches while optimizing reasoning quality, retrieval accuracy, latency, cost, safety, and user experience. It also evaluates foundational and open-weight models for enterprise use.

Conduct research and prototype learning-based methods for perception, planning, and control in embodied AI and robotics. Collaborate on navigation, manipulation, orchestration, and real-world robot systems. Evaluate vision and machine learning models for robotics applications and contribute to peer-reviewed publications, patents, prototypes, and other technical outcomes.

Leads customer-embedded AI engineering engagements, architecting and prototyping solutions across Cisco and Splunk technologies in complex enterprise environments. The role accelerates customer business outcomes, integrates AI infrastructure with distributed systems, collaborates with product engineering, influences technical decision-makers, and mentors forward-deployed engineering teams. Responsibilities span generative and agentic AI, machine learning platforms, Kubernetes, event-driven architectures, and cloud-native and hybrid deployments.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.