Join us to transform wholesale credit risk with cutting-edge AI solutions that have real impact. This role offers the chance to work with advanced machine learning and generative AI technologies in a fast-paced environment. You will collaborate closely with diverse teams to bring innovative ideas from concept to production. Your work will directly strengthen risk management and decision-making across the firm. If you enjoy building reliable, high-impact AI tools, this opportunity offers both scope and visibility.
As an Applied AI Lead Data scientist within Wholesale Credit Risk Quantitative Research, you will design and deliver generative AI and agentic solutions, including LLM-powered agents, multi-agent orchestration, reasoning loops, and retrieval-augmented systems that transform the end-to-end wholesale credit risk process. Additionally, you will work closely with cross-functional partners, you will translate business needs into scalable, production-ready capabilities, build model-agnostic agent harnesses that combine persistent memory, tool use, and context management and uphold rigorous standards for performance, safety, and reliability across the full model lifecycle.
Job responsibilities
Develop and implement applied AI and machine learning solutions — spanning generative AI, agentic workflows, and traditional ML — that address core wholesale credit risk challenges.
Build LLM-powered agents and multi-agent systems with capabilities such as planning, parallel sub-task execution, entity resolution, and human-in-the-loop escalation.
Design context management strategies — including retrieval, isolation, compaction, and offloading — to maintain quality across long-running analyses.
**Partner **with cross-functional teams to translate business requirements into technical designs and concrete deliverables.
Lead solution delivery across the full lifecycle, evolving capabilities from POC to autonomous skill execution.
Build verification and validation loops that check agent outputs against business rules before delivery.
Prepare and present clear, stakeholder-ready materials covering objectives, methodology, results, and limitations.
Monitor deployed solutions and continuously evaluate model performance, stability, and drift through regression testing and evaluation datasets.
Required qualifications, capabilities, and skills
Advanced degree in data science, computer science, engineering, mathematics, or statistics.
Minimum 5 years of experience in applied artificial intelligence and machine learning.
Strong practical understanding of machine learning methods and model development.
Proficiency in Python (including modern scientific computing workflows).
Hands-on experience with at least one deep learning framework (TensorFlow, Keras, or PyTorch).
Experience working with large-scale data using tools such as Spark.
Proficiency in SQL for data extraction and analysis.
Strong problem-solving skills with the ability to break down ambiguous business problems.
Strong written and verbal communication skills, including explaining technical concepts to non-technical audiences.
Strong collaboration skills and ability to deliver in a cross-functional environment.
Preferred qualifications, capabilities, and skills
- Expertise in natural language processing and large language model techniques.
- Experience implementing models in production and supporting post-deployment monitoring.
- Cloud experience (for example, building or deploying solutions in cloud environments).
- Background in financial services and familiarity with credit risk concepts.
- Experience building solutions influenced by macroeconomic signals and fast-changing external events.