We’re looking for an innovative, passionate and experienced data scientist to be part of the growing International Private Bank Technology Artificial Intelligence and Machine Learning (AI/ML) Team. Join the ranks of top talent at one of the world’s most influential companies.

As an Applied AI/ML Lead, you will have the opportunity to work on state-of-the-art Language Modelling projects that will transform the operating model for the IPB and Wealth Management business. The key focus areas within this position will be on prompt engineering for Large Language Models (LLMs), and supporting the development and deployment of prompt-based models for various Natural Language Processing (NLP) tasks such as text classification, question answering, and language generation on a rich universe of financial and market data including unique datasets which can be leveraged to define the experiences for our clients and employees across the globe.  An excellent communicator, you will work across agile teams and liaise with IPB business and product stakeholders to develop and optimize AI/ML solutions. You will be a strong proponent of an AI/ML ‘aware’ culture within the IPB, championing the advancement and broader education of IPB developers in the AI/ML field.

Job responsibilities

Design, deploy and manage prompt-based models on LLMs for various NLP tasks within the International Private Bank. Conduct research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field. Collaborate with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization. Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs. Develop and maintain tools and framework for prompt-based model training, evaluation and optimization. Analyze and interpret data to evaluate model performance to identify areas of improvement. Advise on the technical feasibility and business need for International Private Bank AI/ML use cases. Guide and advise developers through the Machine Learning Development Lifecycle (MDLC). Be proactive in the broader education of IPB developers in AI/ML development and drive a conducive AI/ML ‘aware’ culture within the IPB. Translates highly complex technical issues, trends, and approaches to leadership to drive the firm’s innovation and enable leaders to make strategic, well-informed decisions about technology advancements. Influences across business, product, and technology teams and successfully manages senior stakeholder relationships.

Required qualifications, capabilities, and skills

Formal training or certification on applied AIML concepts and advanced applied experience Master’s degree or higher in Computer Science, Engineering or related field. Programming skills in Python with experience in PyTorch or TensorFlow. Thorough knowledge of deep learning concepts, including attention mechanisms, transformers, and language modelling. Experience in data pre-processing, feature engineering, and data analysis. Excellent problem-solving and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner. Ability to work in a fast-paced environment on multiple projects simultaneously. Basic knowledge of deployment processes, including experience with GIT and version control systems for efficient collaboration and code management in MLOps projects. Familiarity with data structures and algorithms, enabling effective problem-solving and optimization in machine learning workflows. Hands on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environments. Extensive practical cloud native experience.

Preferred qualifications, capabilities, and skills

Experience in developing and deploying production-grade NLP models in the financial services industry. Knowledge of financial products and services including trading, investment and risk management. Familiarity with machine learning frameworks like scikit-learn and Keras. Experience in developing APIs and integrating NLP models into software applications Comfortable working in a cloud environment like AWS/GCP or Azure. Passionate about working with large unstructured and structured datasets.