As a member of the Commercial and Investment Banking Applied Artificial Intelligence/Machine Learning team, you will have the unique opportunity to be a critical player in our firm-wide efforts to shape the future of banking. Within this role, you will help transform how Know Your Customer and operations teams function, directly impacting the management and operations of the bank's corporate and investment banking services. You will be a key member of a team composed of data scientists, operations subject matter experts, and machine learning engineers, working together to design, develop, and deploy scalable machine learning solutions. Our vision is to create products that transform how the firm operates, deliver measurable impact, and have the potential for commercialization. A finance background is not required. If you are enthusiastic about leveraging machine learning and analytics to solve challenging business problems, we’d love to speak with you.

Responsibilities:

• Research and develop innovative ML based solutions to some of KYC and operations hardest problems.
• Build robust data science capabilities which can be scaled across multiple business use cases.
• Collaborate closely with business domain experts in a partnership framework, ensuring clear communication and fostering trust among stakeholders.
• Collaborate with software engineering teams to design, deploy, and maintain production-grade ML models that can be integrated with strategic systems.
• Research and analyze large data sets using a variety of statistical and machine learning techniques.
• Offer technical mentorship and guidance to team members, sharing industry best practices and staying updated with the latest advancements in machine learning.
• Communicate AI capabilities and results to both technical and non-technical audiences.
• Document approaches taken, techniques used, and processes followed to comply with industry regulation.

Required qualifications, capabilities & skills

• Master’s degree or PhD in a quantitative or computational discipline
• Considerable commercial experience in line with a capable individual contributor; developing and deploying data science and ML capabilities in production at scale.
• Strong Python development and debugging skills. Capable to develop high quality reusable code that can be leveraged from a larger group of data scientists to solve a broad spectrum of business use cases.
• Strong grasp of metrics, benchmarking, and evaluation methodologies for user-facing products powered by AI/ML.
• Deep knowledge of machine learning algorithms applied to solving business problems.
• Ability to work both individually and in collaboration with others, and to mentor junior team members.
• Posses a strategic mindset capable of deconstructing business challenges into solutions driven by AI.
• Ability to work in agile cross-functional and operations teams and drive deliverable outcomes.
• Ability to work with non-specialists in a partnership model, conveying information clearly and creates a sense of trust with stakeholders.

Preferred qualifications, capabilities & skills

• Experience with deep learning frameworks (pytorch, tensorflow)
• Experience with big-data technologies (Spark, Hadoop) or distributed computation frameworks (Dask, Modin)
• Hands on experience with Natural Language Processing (NLP), Large Language Models (LLMs) and Agentic AI systems.
• Experience of creating and deploying microservices
• Knowledge of MLOps concepts (CI/CD, versioning, reproducibility, observability) and development best practices