Data Scientist

Senior Associate, Data Scientist - Model Risk Office Location: Chicago, Illinois, United States (Onsite) Industry: Financial Services Work Setting: Onsite Are you passionate about leveraging advanced analytics, machine learning, and AI technologies to solve complex business challenges and drive better decision-making at enterprise scale?

We are seeking a Data Scientist to develop innovative machine learning solutions, evaluate model performance and risk, and uncover actionable insights from large, complex datasets. This role combines statistical modeling, AI/ML development, model validation, and business partnership to support strategic initiatives while ensuring robust, reliable, and responsible use of analytical models.

Data Science & Advanced Analytics

Develop, test, and deploy machine learning and statistical models that support critical business decisions. Analyze large, complex, and multi-dimensional datasets to identify patterns, trends, and business opportunities. Apply advanced analytical techniques to solve challenging business and risk-related problems.

Leverage quantitative analysis to generate actionable insights and recommendations for stakeholders.

Machine Learning & AI Solutions

Design, build, and evaluate machine learning models across a variety of use cases and business domains. Utilize modern AI and machine learning frameworks and technologies to develop innovative solutions. Support the implementation and optimization of predictive, classification, clustering, sentiment analysis, and time-series models.

Explore emerging AI technologies, including generative AI, large language models, and advanced analytics capabilities. Model Validation & Risk Assessment Evaluate model performance, assumptions, methodologies, and underlying data quality. Perform model validation, back-testing, benchmarking, and control assessments.

Identify, assess, and communicate model risks and potential business impacts. Develop challenger models and independent assessments to validate model effectiveness. Contribute to governance practices and model risk management frameworks.

Data Engineering & Analytics Collaboration

Work with structured, unstructured, and multi-modal datasets from a variety of internal and external sources. Collaborate with engineering teams to develop scalable analytical and machine learning solutions. Support data preparation, feature engineering, model training, testing, and deployment activities.

Ensure analytical solutions are reproducible, scalable, and operationally efficient.

Cross-Functional Partnership

Partner closely with data scientists, software engineers, product managers, business leaders, and domain experts. Translate business challenges into analytical approaches and measurable outcomes. Collaborate with stakeholders to prioritize opportunities and develop data-driven solutions.

Present findings, insights, and recommendations to technical and non-technical audiences. Research, Innovation & Continuous Improvement Stay current with emerging developments in machine learning, artificial intelligence, statistics, and data science. Research and evaluate new methodologies, frameworks, and technologies.

Identify opportunities to improve existing models, processes, and analytical capabilities. Promote innovation through experimentation and evidence-based decision-making. Executive Communication & Storytelling Communicate complex analytical findings in a clear, concise, and business-focused manner.

Prepare presentations, reports, and visualizations that help stakeholders understand risk, performance, and opportunities. Influence business decisions through compelling data-driven storytelling. Explain technical concepts and model outcomes to leadership and executive audiences.

Required Qualifications

• Bachelor's degree in Statistics, Mathematics, Economics, Analytics, Operations Research, Computer Science, Data Science, or another quantitative discipline with relevant analytical experience; or Master's degree in a quantitative field or MBA with a quantitative concentration.
• Experience performing data analytics, statistical analysis, or machine learning projects.
• Strong quantitative, analytical, and problem-solving skills.
• Experience working with large and complex datasets.
• Strong communication and presentation skills.

Preferred Qualifications

• Master's degree or PhD in a STEM-related field.
• Experience with cloud platforms such as AWS.
• Experience using Python, R, Scala, or similar programming languages for data analysis and model development.
• Experience developing and deploying machine learning models in production environments.
• Knowledge of modern AI, deep learning, and generative AI techniques.
• Experience working in highly data-driven or regulated industries.
• Preferred Skills Data Science Machine Learning Generative AI Large Language Models (LLMs) Model Risk Management Statistical Modeling Predictive Analytics Deep Learning Python R Scala Data Mining Feature Engineering Time Series Analysis Classification & Clustering Model Validation AWS Cloud Services Data Visualization Business Analytics Cross-Functional Collaboration