The Consumer and Community Bank business needs to enhance operations through intelligent solutions, requiring the design, development, and scaling of machine learning and AI workflows to address strategic challenges in front-to-back modeling, automation, agents, and decision support.
Requirements
- Proficiency in Python, PyTorch, TensorFlow, Scikit-learn, Jupyter; hands-on with LLM agent frameworks, deep learning (CNNs, transformers), exploratory data analysis.
- Experience with MLOps, model monitoring, cloud (AWS, Azure), Spark/PySpark, or Databricks.
- Understand data structures, algorithms, scalable system design, and production practices.
- Familiarity with prompting techniques, fine-tuning, multi-modal agent workflows, APIs for LLMs.
- Deep learning (CNNs, transformers)
- LLM agent frameworks
- Responsible AI frameworks: fairness, bias mitigation, explainability.
Responsibilities
- Guide a dedicated AI team to explore advanced ML and AI techniques such as, LLMs, generative AI, agentic systems, to address strategic Consumer and Community Banking challenges in front-to-back modeling.
- Architect and implement state-of-the-art machine learning pipelines, scalable services, and APIs using Python, PySpark, DBX, and more.
- Research, prototype, deploy, and monitor ML models, including classification, regression, transformer-based LLMs, and multi-modal agentic workflows.
- Work with Product, Consumer Banking, Finance, Compliance, Technology, Legal, and CDAO to deliver AI and ML-powered automation, agents, and decision support.
- Ensure robust model risk documentation, regulatory compliance, fair and responsible AI, monitoring, and version control frameworks.
- Coach a hybrid team of ML engineers, data scientists, and research technologists, fostering best practices in MLOps and Python-driven experimentation.
- Translate model outputs into business KPIs, deliver performance insights to senior leadership, and drive ROI and adoption across CCB.
Other
- Master's in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field; 5+ years in the industry as a data scientist/ML engineer, including lead roles building AI/ML applications in tech or financial services.
- Strategic thinking, clear communication across technical and non-technical audiences, ability to translate OKRs, mentor teams, and influence stakeholders.
- PhD preferred for deep-Lab/agentic use cases
- Experience in financial institution environments.
- Publications or patents in ML/AI, particularly agentic or generative systems.
