As a Vice President – Credit Risk Data Science, Business Banking Risk Modeling, you will lead advanced feature engineering and machine learning initiatives that power customer analytics and credit risk decisioning across Chase sub-lines of business. You will own the end-to-end design and scaling of an enterprise Risk Attribute Library, ensuring feature quality, lineage, and reusability, while building and improving production-grade models that influence critical business decisions. Starting with a focus on the card business, you will extend solutions across the broader Chase portfolio and drive innovation using modern analytics, deep learning, and large language model enabled approaches.

Job Responsibilities:

• Lead the design, development, and governance of a scalable enterprise Risk Attribute Library, owning end-to-end attribute and model quality.
• Drive consistency, reliability, and reuse of features across customer lifecycles and Chase sub-lines of business through rigorous standards and reproducible development practices.
• Partner with cross-functional teams to ideate, prototype, and productionize advanced feature engineering methods.
• Engineer high-impact features from large-scale structured and unstructured datasets to improve predictive performance and decisioning outcomes.
• Build robust machine learning and deep learning models, including Transformer-based approaches, to predict customer behavior and optimize risk strategies.
• Apply large language model techniques to extract signal from unstructured text (for example, customer interactions, disclosures, narratives) to enhance models and enable new analytics products.
• Establish attribute quality testing and monitoring frameworks to detect data drift, leakage, instability, and distribution shifts.
• Implement alerting mechanisms and resolve data or feature issues to maintain accuracy, stability, and consistency in production.
• Evaluate new internal and external data sources by assessing signal strength, stability, latency, and compliance considerations.
• Align with risk, marketing, technology, data governance, and controls partners to ensure correct implementation and robust documentation, lineage, and lifecycle management.
• Communicate complex analytical findings clearly to technical and non-technical stakeholders, translating results into actionable recommendations and measurable business impact.
  

Required Qualifications, Capabilities, and Skills:

• Master’s degree or Doctor of Philosophy degree in Computer Science, Mathematics, Statistics, Econometrics, Engineering, or a related quantitative discipline.
• 5+ years of experience working with large-scale data and developing, managing, or implementing attributes and predictive models.
• 5+ years of professional coding experience with demonstrated ability to write high-quality, production-ready code.
• Proficiency in one or more of the following: Python, Statistical Analysis System, Apache Spark, Scala, or equivalent data and machine learning programming stacks.
• Experience with modern machine learning and deep learning frameworks and platforms such as TensorFlow (or equivalent), Amazon Web Services cloud, Snowflake, and or Databricks.
• Strong understanding of statistical and machine learning methods such as generalized linear models and regression, decision trees, random forests, boosting, clustering, k-nearest neighbors, anomaly detection, simulation, scenario analysis, and modeling.
• Demonstrated ability to perform feature engineering, model validation, and performance evaluation in a regulated or controlled environment.
  

Preferred Qualifications, Capabilities, and Skills:

• consumer lending experience strongly preferred