Delivers Generative AI-based models of high complexity to surface insights; drives action, defines success measures and tracks performance of models. Develops monitoring metrics to reflect real-world model performance and makes recommendations to shape the future direction of models and digital solutions. Strategizes models to uncover patterns and predictions creating business value and innovation. Manages relationships with business partners to evaluate and foster data-driven innovation, provides domain-specific expertise in cross-organization projects/initiatives. Ties insights into effective visualizations communicating business value and innovation potential.
Works with development teams and business units to ensure models are scalable and can be implemented as part of the delivered solution. Leads project team(s) of data science professionals, assuring insights are communicated regularly and effectively, reviewing designs, models and data compliance. Represents the data science team for all phases of larger and more-complex development projects.
Provides guidance, training and mentoring to less experienced staff members.
Requirements
PhD related to AI or a Masters degree in computer science or a related field and 5+ years of experience with GenAI and related technologies including machine learning, data analytics, and statistical modeling. Strong foundation in data science and core machine learning concepts. Experience with large language models, multi-modal modals, prompt engineering, vector databases, Retrieval-Augmented Generation (RAG), and search engines. Solid technical background.
Experience working on software development teams, especially in cloud-based business applications, is a strong plus. Familiarity with tools such as Azure Machine Learning, Azure Prompt Flow, Python, LangChain, Streamlit, and Docker. Experience working in distributed, diverse teams, with bonus points for time spent in small fast-paced start-ups.
Comfortable representing the team in meetings with multiple stakeholders and driving discussions clearly and respectfully. Mastery of English is required. Able to explain technical concepts (e.g., LLMs) to non-technical audiences. You suggest pragmatic solutions that are as simple as possible-even if that means no AI is required.