Senior Vice President, Data Science Manager

BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide. Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary. We’re seeking a future team member for the role of Data Science Manager, Revenue Analytics in Asset Servicing. This role is located in Boston. BNY is seeking an SVP, Data Science Manager within Asset Servicing Deal Management and Controls to lead strategic initiatives at the intersection of data science, knowledge engineering, natural language processing, and applied AI . This role will focus on transforming how proposals, RFP, due diligence, and related controlled content is structured, governed, retrieved, and reused across Asset Servicing. The successful candidate will lead the development of a governed, scalable content ecosystem that improves the quality, consistency, speed, and completeness of first-draft responses , while reducing manual effort and unnecessary subject matter expert outreach. This role combines data science leadership with a strong knowledge engineering focus , applying advanced analytical and AI methods to business text, response content, and approved firm artifacts to improve response generation, content quality, and operational efficiency. This role will apply semantic search, sentence embeddings, similarity scoring, classification, clustering, duplicate detection, summarization, metadata tagging, named entity recognition, information extraction, answer recommendation, and content gap identification to improve knowledge reuse and proposal effectiveness.

Knowledge Engineering and Content Optimization Lead the transformation of the Asset Servicing proposal knowledge base to improve first-draft quality, consistency, speed, and completeness across client opportunities. Design scalable approaches to structure, govern, enrich, and optimize reusable proposal, due diligence, and controlled content, including Q&A pairs, reusable response modules, product descriptions, service language, and other approved firm artifacts. Develop methods to organize content so it communicates technical, operational, product, service, risk, and control-related information clearly, accurately, and persuasively. Establish content governance standards across taxonomy, ontology, metadata models, content schemas, lifecycle management, editorial quality, approvals, and version control. Integrate and normalize diverse content sources into a unified, governed, and analytically manageable content ecosystem spanning structured and unstructured text assets. Applied AI, NLP, and Retrieval Intelligence Apply advanced NLP, text analytics, machine learning, and AI methods to improve response drafting, semantic retrieval, content reuse, and language quality. Develop approaches using semantic search, sentence embeddings, similarity scoring, document classification, clustering, duplicate detection, topic extraction, summarization, metadata tagging, named entity recognition, and information extraction. Build scoring, ranking, and answer recommendation frameworks to identify the most relevant, current, high-quality, and reusable content for specific proposal and due diligence use cases. Create frameworks to evaluate and improve multiple forms of business language, including technical explanatory content, service model descriptions, control and risk language, product capability statements, proof points, differentiators, and persuasive