Top AI & Machine Learning Jobs in Chicago, IL

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.

Build and deploy machine learning and AI models for the Global Payments Network. Partner with cross-functional teams to design, train, evaluate, validate, and implement models using cloud and open-source tools. Drive innovation, translate technical work to business impact, and mentor talent.

The role involves leveraging technologies like Python and AWS to develop statistical and machine learning models, translating complex data insights into business goals, and working with a cross-functional team.

Lead the Cash Flow Underwriting Team as a Data Scientist, leveraging advanced machine learning techniques for predictive modeling in creditworthiness and customer behavior.

Lead the Anti-Money Laundering Modeling team to develop advanced analytics and machine learning models, partnering with cross-functional teams to derive actionable insights from data.

Lead product management for Capital Ones Enterprise AI/ML platform, building intelligence capabilities and tools for data scientists and ML engineers. Drive roadmap, cross-functional execution with engineering, risk, security, and business partners, ensure governance, resiliency, and cost controls, and deliver scalable, user-centered platform experiences.

The Senior Manager will lead data science initiatives in fraud detection, collaborating with data scientists and engineers to build machine learning models and mentor junior staff.

The role involves partnering with data scientists and engineers to develop machine learning models, evaluate model risk in fraud detection, and translate complex data insights into business goals.

Lead development and evaluation of statistical and machine learning models to assess credit risk and challenge production models. Use Python, cloud, and big-data tools to analyze large numeric and textual datasets, partner with cross-functional credit risk and product teams, translate technical findings into business actions, and drive model validation, backtesting, and innovation.