Top Machine Learning Jobs in Chicago, IL

Develop and validate machine learning models for compliance and model risk management. Partner with data scientists, software engineers, and product managers to analyze large numeric and textual datasets using Python, cloud, and big-data technologies. Responsibilities span model design, training, evaluation, validation, implementation, statistical analysis, and translating technical findings into business outcomes.

Leads data science and machine learning initiatives for a global payment network. Partners with data scientists, software engineers, and product managers to develop scalable models and AI tools for fraud, risk, merchant acceptance, intelligence, and monitoring. Oversees model design, training, evaluation, validation, and implementation while translating technical insights into business outcomes and developing team talent.

Leads advanced data science initiatives for Expedia Group Advertising, applying statistical analysis, experimentation, machine learning, and data modeling to optimize advertising products, auctions, bidding, measurement, and incrementality. Partners with product, engineering, and business teams to define metrics, guide decisions, build scalable solutions, evaluate AI/ML performance, and communicate insights. Mentors data scientists and engineers while influencing technical strategy and product outcomes.

Lead advanced digital and rental analytics using GA4, BigQuery, Python, and SQL. Analyze clickstream data, customer journeys, conversion funnels, attribution, and site performance to improve revenue and customer growth. Define KPIs, develop analytics roadmaps, apply statistical and machine learning methods, establish data governance, and translate insights into recommendations. Collaborate with senior leaders, product owners, strategy teams, and technical stakeholders while supporting scalable measurement solutions and data-driven product development.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Lead AI and autonomous vehicle behavior safety strategy for Level 3–4 automated driving systems. Own behavior safety claims, evidence criteria, validation methodologies, performance indicators, and launch-readiness assurance. Evaluate ML-driven AV performance using simulation, closed-course, and public-road data. Define safety metrics and strategies for imitation and reinforcement learning systems, interpret applicable standards, and influence cross-functional engineering teams and executives.

Lead data science and model risk initiatives by partnering with data scientists, engineers, and product managers. Build, evaluate, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical methods, backtesting, and model-risk expertise while translating complex analyses into business outcomes. Responsibilities also include researching emerging technologies, improving decision-making, and developing talent.

Conduct applied AI research and develop automation, knowledge management, semantic search, retrieval, and reasoning systems. Build and evaluate proof-of-concepts, create frameworks for assessing AI risks and business impact, and collaborate with engineering teams to productionize successful concepts. The role explores agentic, causal, explainable, neuro-symbolic, and knowledge-enhanced AI, while contributing to publications and patents across financial services, fraud, risk, insurance, and marketing applications.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead teams building Samsara Assistant, Agent Studio, and agent extensibility. Own product growth, activation, answer quality, trust, repeat usage, and proactive agent capabilities. Set technical direction for an AI platform used by customers and internal product teams, partner across Safety and Maintenance, and lead approximately 10 machine learning, applied science, and full-stack engineers through two tech leads. Hire, develop, and manage a high-performing engineering organization.

Leads a data science sub-team evaluating John Deere technologies through performance benchmarks, models, analyses, and data pipelines. Engineers features from machine, operational, environmental, and usage data; conducts exploratory analysis; and translates findings into product recommendations. Partners with engineering and business leaders, mentors technical staff, maintains evaluation evidence, and communicates results to senior stakeholders while working in an Agile environment.

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.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

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.

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.

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.

Build and deploy enterprise AI/ML solutions, including generative AI, agentic systems, RAG pipelines, LLM-powered chatbots, and model-serving microservices. Develop model training, evaluation, monitoring, and production integration workflows while collaborating with data science, engineering, MLOps, and product teams. Prototype emerging AI techniques, improve scalability and performance, and support critical IT escalations. The role requires strong Python and machine learning expertise, with preferred experience in Azure, Databricks, Kafka, Kubernetes, and GPU-based model deployment.