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.
Finance background is not a must-have. If you get as excited about machine learning theory as you get about Python development, we’d love to speak with you.
In this role, you will:
- Interact with very large datasets from different businesses in the financial domain currently not available anywhere else.
- Write production-ready code and work with tech teams to ensure your ML solution is deployable at scale across multiple lines of business.
- Develop products that can change how corporate and investment banking is done today.
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 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.
- 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
- Bachelors/Masters 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.
- Ability to work both individually and in collaboration with others, and to mentor more junior team members.
- 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