Top Fintech Data Science Jobs in Chicago, IL

The Data Scientist will enhance actuarial processes through statistical models and machine learning, collaborating with cross-functional teams and communicating results effectively.

Lead the development and implementation of advanced data science models, ensuring data quality while mentoring junior scientists and collaborating with various stakeholders.

Lead design, development, and maintenance of fraud-risk and predictive models, architect ETL and storage for large datasets, manage analytic projects and teams, document solutions, research new techniques, and communicate technical results to stakeholders.

The Data Science Intern will support the Data Science team by conducting research, performing data analysis, and collaborating with team members to solve business problems using statistical and machine learning techniques.

The Sr. Data Scientist will optimize risk solutions using machine learning and statistical methods, develop models, mentor juniors, and collaborate with stakeholders on credit risk management.

The Data Science Actuary role involves building and maintaining rating systems and risk models, conducting analysis for underwriting and operations, and integrating new data sources while collaborating with multiple teams.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

The Staff Data Scientist will develop machine learning models, collaborate with cross-functional teams, and use analytics to drive business growth and improve credit risk assessment.

As a Principal Data Scientist, you'll design and deploy cutting-edge machine learning models to drive financial success for customers and guide technical teams.

As an AI-First Data Scientist, you will design and deploy ML solutions, conduct causal analysis, and automate intelligence in decision-making processes. You will work closely with senior operators and business leaders, translating complex problems into data-driven solutions while ensuring model performance and scalability.