Job Summary
The Data Scientist will work across data science and AI/ML engineering to build, evaluate, and improve AI/ML solutions, including Generative AI (GenAI) applications, that support corporate services and business functions. The role involves developing scalable, practical solutions to improve operational efficiency and reduce risk, including use cases supporting Global Risk and Security. The Data Scientist will collaborate with cross-functional teams, business stakeholders, product leaders, and external vendors to translate business requirements into responsible, production-oriented AI solutions.
Key Responsibilities
• Build, test, and improve AI/ML solutions, including GenAI use cases, for practical business applications.
• Collaborate with product leaders, business stakeholders, and technical teams to translate requirements into implementable AI features and model enhancements.
• Support the development and evaluation of AI models, reviewing models in progress for quality, performance, and fitness for purpose.
• Contribute to model quality processes, including automated evaluation pipelines and manual review workflows.
• Implement and optimize model training and inference workflows to support scalable deployment.
• Communicate technical concepts clearly to technical and non-technical stakeholders.
• Contribute to knowledge sharing and collaboration across teams.
• Partner with cross-functional teams and external vendors to deliver AI/ML solutions aligned with business objectives and responsible AI practices.
Required Qualifications
• Master's or Ph.D. in Computer Science, Machine Learning, Operations Research, or a related field.
• 3+ years of industry experience implementing Natural Language Processing (NLP) or GenAI solutions, including building, scaling, and optimizing training and inference APIs for deep neural networks.
• 5+ years of coding experience using languages and frameworks such as Python and PySpark.
• Strong understanding of the GenAI landscape, including recent advancements, emerging trends, and responsible AI practices.
• Strong communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Experience working with cross-functional teams and balancing technical implementation with business objectives.
