CCB Risk Modeling - Applied AI ML Senior Associate

Data Science & Model Insights - AI ML Sr. Associate The Data Science & Model Insights team is seeking talented individuals with expertise in machine learning, big data, and distributed computing, specifically for applications within credit decision and fraud modeling. The ideal candidate will contribute to long-term profitable growth by leveraging strong business acumen, collaborating effectively in a team environment, and communicating insights to senior management. Job Responsibilities: Design and develop machine learning models to drive impactful decisions across credit decision and fraud modeling. Research, develop, document, implement, maintain, and support tools and frameworks that enhance AI/ML model explainability and fairness, ensuring transparency and ethical use of models. Utilize state-of-the-art machine learning methodologies and construct sophisticated models, including deep learning architectures, on big data platforms to solve complex business challenges. Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments. Collaborate with diverse teams, including marketing, risk, technology, model governance, and research, throughout the entire modeling lifecycle—from development and review to deployment and operational use. Required qualifications, capabilities and skills: Ph.D. or Master’s degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering. Proven track record in designing, building, and deploying high-quality machine learning models in production environments, demonstrating a strong ability to translate theoretical concepts into practical applications. In-depth knowledge of advanced machine learning algorithms, including regressions, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model design and hyperparameter tuning. Experience in interpreting complex machine learning models such as XGBoost and GBM, with additional experience in interpreting deep learning models considered a valuable asset. At least one year of hands‑on experience and proficiency in programming languages and frameworks such as Python, TensorFlow, Spark, or Scala, coupled with expertise in big data technologies like Hadoop, Teradata, AWS Cloud, and Hive. Preferred qualifications, capabilities and skills: Strong expertise, interest, and track record of performing cutting‑edge research on Explainable AI (XAI). Familiarity with large language models (LLMs) and their applications, including experience in fine‑tuning and deploying LLMs for natural language processing tasks. Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is desired. Strong ownership and execution; proven experience in implementing models in production. #J-18808-Ljbffr JPMorgan Chase & Co.