What you will do

We are a leading international bank focused on helping people and companies prosper across Asia, Africa and the Middle East.

To us, good performance is about much more than turning a profit. It's about showing how you embody our valued behaviors - do the right thing, better together and never settle - as well as our brand promise, Here for good.

We're committed to promoting equality in the workplace and creating an inclusive and flexible culture - one where everyone can realize their full potential and make a positive contribution to our organization. This in turn helps us to provide better support to our broad client base.

About the Team

Financial Markets (FM) has expertise combined with deep local market knowledge to deliver a variety of risk management, financing and investment solutions to our clients. The FM team offers capabilities across origination, structuring, sales, trading and research. Offering a full suite of fixed income, currencies, commodities, equities and capital markets solutions, FM has firmly established itself as a trusted partner with extensive on-the-ground knowledge and deep relationships.

• Streamlines and automates data science products lifecycle from development to deployment and monitoring.
• Designs, develops and maintains tools enabling reproducible experimentation, models versioning, automated deployment, serving etc.
• Tests, refactors, optimizes, and packages the code developed by data scientists. Improves, deploys, monitors and maintains developed models.
• Builds new integrations with FM systems (PDS, ION, S2BX) allowing to publish signals generated by the data science modules.
• Provides support and guidance to other team members on MLOps best practices.

Requirements

MUST

Must-Have:

• Minimum 3 years hands-on experience building, deploying and maintaining classical supervised ML models in production (preferably on-prem)
• Strong proficiency in Python for production development, including packaging, environment management (pip/poetry), dependency management (requirements.txt), and modular code design
• Strong experience with analytics and ML libraries: numpy, pandas, scikit-learn, and at least one deep learning framework (TensorFlow / Keras / PyTorch)
• Practical experience with MLOps workflows, including experiment tracking, model registry, and model deployment using MLflow / Databricks
• Solid understanding of Linux environments (preferably RedHat), including command line, processes, file system, and debugging
• Experience with containerization (Docker/Podman) and ability to explain when and why containers are used
• Working knowledge of CI/CD pipelines, including their purpose, structure, and mechanics (preferably Azure DevOps Services)
• Ability to describe and apply ML model monitoring, including key metrics (drift, performance, latency) and operational considerations
• Ability to work independently across the full ML lifecycle (data - model - deployment - monitoring)

NICE TO HAVE

Nice-to-Have:

• Experience with SQL-based data modelling and database administration (preferably PostgreSQL)
• Experience with orchestration tools (Airflow or similar)
• Familiarity with distributed processing (Hadoop, Spark/PySpark)
• Familiarity with kdb+/q
• Understanding of financial markets, especially fixed income