We’re interested in hearing from people with the following skills.
· Technical Leadership and Coordination:
o Ability to coordinate effectively across the Data Science community to unify efforts and drive collaborative outcomes.
o Demonstrated capability in driving outcomes that are reusable and can be leveraged across the group.
o Intellectual curiosity and ability and willingness to learn, experiment and lead new to the world concepts
Technical Skills and Tools:
o Machine Learning: Expertise in the design and construction of machine learning models, including feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment of predictive models into production environments.
o Generative AI: Familiarity with various aspects of Generative AI, including prompt engineering, Retrieval-Augmented Generation (RAG), guardrail design, orchestration design, and tools such as LangChain and LangGraph.
o Cutting-Edge AI: Work on projects leveraging traditional AI, Generative AI, and agentic AI to pave the path for how banking and broader industry functions in the future.
o Version Control and CI/CD: Experience with version control and CI/CD pipelines such as GitHub.
o Tooling: Hands-on experience with data science tools such as Spark, Python, R, TensorFlow, PyTorch, and SQL.
o Data Engineering: Knowledge of ETL processes and data pipeline construction, ensuring efficient data flow and integration.
o Deployment: Familiar with Docker for containerization and deployment of applications.
o Cloud Architecture: Grounded understanding in AWS or Azure Cloud architecture and integration, demonstrating experience in deploying and managing cloud-based solutions.