Risk Management-Gen AI- Lead Data Scientist- Vice Preseident

Are you looking for an exciting opportunity to solve large-scale business problems using Generative AI? Join our dynamic team to tackle these challenges as part of the Wholesale Credit Risk Quantitative Research – Applied AI/ML team. You will develop innovative AI solutions leveraging the firm's extensive data resources, focusing on creating tools based on Large Language Models (LLMs) to enhance the End-to-End credit risk process across Wholesale. This role offers a unique chance to innovate and make a significant impact in credit risk management. If you are passionate about AI and eager to work on cutting-edge solutions, we encourage you to apply.

As a Gen AI Data Scientist in the Wholesale Credit Risk Quantitative Research – Applied AI/ML team, you will develop AI solutions to address business challenges and enhance the credit risk process. You will collaborate with cross-functional teams to translate requirements into technical solutions and manage the full lifecycle from Proof of Concept to production-ready solutions. Your work will ensure the performance and reliability of deployed solutions, staying informed on the latest AI/ML advancements.

Job Responsibilities

• Develop and implement AI solutions to address business challenges.
• Collaborate with cross-functional teams to translate requirements into technical solutions.
• Formulate risk strategies to enhance risk monitoring using diverse data sources.
• Manage the full lifecycle from Proof of Concept to production-ready solutions, including stakeholder presentations and post-implementation monitoring.
• Ensure the performance and reliability of deployed solutions.
• Stay informed on the latest AI/ML advancements.
• Lead the development and rapid deployment of AI solutions influenced by macro-economic factors and current events.

Required qualifications, capabilities, and skills

• Advanced degree in Data Science, Computer Science, Engineering, Mathematics, or Statistics.
• Minimum of 5 years of experience in applied AI/ML.
• Strong understanding and practical experience with Machine Learning; expertise in LLM/NLP.
• Proficiency in modern analytic and data tools, especially Python/Anaconda, TensorFlow, Keras/PyTorch, Spark, and SQL.
• Experience in model implementation and production deployment.
• Excellent problem-solving, communication, and teamwork skills.

Preferred qualifications, capabilities, and skills

• Cloud experience.
• Background in financial services.