Top AI & Machine Learning Jobs in Chicago, IL

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.

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.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

Manage a team to assess model risks, build and validate machine learning models while leveraging advanced technologies and cross-functional collaboration.

Lead a team to analyze data, create machine learning models, and deliver insights for customer-focused financial products, leveraging various technologies.

Lead development of production machine learning models for co-branded card decisioning. Partner with cross-functional teams, use Python, Spark, H2O, and AWS to design, train, validate, and deploy models for approve/decline, product optimization, and customer valuation.

Lead a workstream building production machine learning models for credit decisioning. Partner with cross-functional teams to design, train, validate, and implement models using large-scale data, cloud platforms, and open-source tools. Translate technical results into business recommendations and develop talent within the team.

Design, train, validate, and implement machine learning models using large-scale transaction and cash-flow data. Engineer predictive features, partner with engineers and product managers, and translate technical results into business outcomes to forecast creditworthiness and customer behavior.

Lead model validation and risk quantification across business domains. Partner with data scientists, engineers, and PMs to build and challenge production models using Python, Spark, H2O, and cloud tools. Drive model governance, backtesting, and present identified model risks to executives while mentoring teams and advancing validation frameworks.