Top Machine Learning Jobs in Chicago, IL

Leads AI initiative planning, governance, delivery, and operationalization across the organization. Establishes intake and execution frameworks, manages portfolios, dependencies, risks, resources, and executive reporting, and coordinates business, architecture, engineering, infrastructure, security, vendors, and governance teams. Defines solution approaches, evaluates build-versus-buy options, develops implementation playbooks, improves processes, and ensures AI initiatives achieve intended business outcomes.

Leads data science and model risk initiatives, partnering with data scientists, engineers, and product managers. Builds, evaluates, validates, and implements machine learning models using large-scale data and technologies including Python, AWS, H2O, and Spark. Translates complex statistical findings into business outcomes, researches emerging methods, validates and backtests models, and develops talent. Requires quantitative education, substantial analytics experience, machine learning expertise, and relational database experience.

Build, validate, and implement machine learning models for Discover Card portfolio underwriting. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies. Partner with data scientists, software engineers, and product managers to deliver customer-focused products. Apply statistical modeling, classification, clustering, sentiment analysis, time series, deep learning, and big-data analysis while translating complex findings for stakeholders.

Analyze large-scale lending operations data, develop predictive models and BI dashboards, automate reporting, ensure data quality, and deliver actionable insights to improve processes, agent performance, risk management, and executive decision-making. The role partners with business and technology teams, applies statistical and machine learning techniques, supports agentic AI strategies, and communicates recommendations to senior leaders.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines across lending products. Analyze large transactional datasets using Python and SQL, partner with Fraud Operations to refine models, reduce false positives and negatives, improve underwriting and verification, apply AI in production, and lead and mentor team members.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Leads AI engineering initiatives for tax technology, designing and deploying machine learning solutions using Python, deep learning, and NLP. Builds data pipelines, validates models, guides experimentation, and translates tax business needs into scalable production systems. Oversees complex workstreams, directs cross-functional teams, presents technical recommendations, coaches team members, and ensures solutions meet technical, client-service, policy, and responsible AI standards.

Develop and refine credit risk, pricing, and predictive models for consumer lending. Design A/B testing frameworks, analyze model performance, collaborate with strategy and operations teams, and present data-driven recommendations to senior management. The role requires statistical analysis, machine learning, Python, SQL, independent problem-solving, and effective communication across teams.

Leads enterprise data science and AI solution delivery, deployment, scaling, and adoption. Oversees machine learning, generative AI, and predictive model development, monitoring, governance, and integration into business workflows. Partners with technology and business stakeholders, evaluates emerging AI technologies, drives change management and value realization, and develops AI delivery teams.

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Build and operate enterprise AI platforms, semantic routing, model gateways, agentic workflows, evaluation harnesses, and production AI services. Develop secure, compliant integrations for regulated financial services, optimize AI workload economics, and establish observability, governance, and reliability standards. Prototype emerging capabilities, create reusable architecture components, mentor engineers, lead technical learning, and partner with security, risk, compliance, and data governance teams.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Leads Caterpillar’s Advanced Technology Pod within AI R&D, setting the applied AI vision and portfolio. Oversees production-intent prototypes, reference architectures, evaluation standards, and responsible AI practices for downstream productionization. Manages senior data scientists and AI engineers, establishes technical standards, and aligns product, engineering, business, dealers, customers, and technology partners. The role requires expertise in modern AI, statistical experimentation, system architecture, production-oriented research, and executive communication.

Define and lead the AI enablement strategy, roadmap, and commercialization plan for TruIQ analytics products. Translate customer needs into product requirements, guide AI-enabled product development, establish evaluation and governance frameworks, and partner with Engineering, Data Science, Security, Compliance, and go-to-market teams. The role promotes responsible AI adoption in regulated environments, validates intelligent capabilities, builds customer trust, and mentors Product Managers on AI product practices.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.

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.

Leads advanced data science projects involving machine learning, deep learning, statistical modeling, forecasting, segmentation, search, recommendations, ranking, and generative AI. Develops production-grade code and evaluation frameworks, deploys scalable models, collaborates with business and technical teams, communicates insights, and mentors junior data scientists.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Leads international data science and analytics product development, managing multiple teams of analytics managers and professionals. Oversees predictive modeling, analytical product delivery, talent development, budgeting, capacity planning, resource allocation, project management, reporting, operational policies, and stakeholder alignment. Requires management of 20+ employees, advanced analytics expertise, customer problem framing, global market knowledge, and formal presentation skills. International travel up to 50% is required.

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.