About the role

As a member of the CIB Cross-Functional Applied AI/ML team, you will have the unique opportunity to be a critical player in our firm-wide efforts to shape the future of banking. Crucial to this is helping to transform how cross-functional and operations teams operate, where you will have a direct impact on the behind-the-scenes management and functioning of the bank's corporate and investment banking services.

You will be a key member of a cross-functional team of data scientists, cross-functions and operations subject matter experts and ML engineers 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.

Finance background is not a must-have. 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 cross-functions 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 Technical Qualifications and Experience

• 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.

Nice to Have

• 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) and Large Language Models (LLMs)
• Experience of creating and deploying microservices
• Knowledge of MLOps concepts (CI/CD, versioning, reproducibility, observability) and development best practices