Senior Vice President, Data Science Manager

BNY Mellon

22h•Onsite

About The Position

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.

Requirements

Bachelor's degree or equivalent work experience with experience preferred in related fields.
Extensive experience in data science, NLP, text analytics, knowledge engineering, knowledge management, content operations, proposal enablement, sales analytics, or related strategic and analytical roles.
Strong experience working with large-scale unstructured text data, document-centric repositories, and enterprise content libraries.
Demonstrated expertise in NLP and language-focused machine learning techniques such as semantic search, sentence embeddings, similarity scoring, classification, clustering, duplicate detection, topic extraction, summarization, named entity recognition, and information extraction.
Experience designing analytical or AI-driven solutions for content that must balance technical accuracy, control sensitivity, regulatory or service-related precision, and clear client-facing communication.
Strong understanding of language quality dimensions such as factual consistency, technical precision, clarity, readability, tone, relevance, persuasiveness, and alignment to approved messaging.
Experience building scoring, ranking, recommendation, or retrieval frameworks for business text based on relevance, freshness, quality, specificity, strategic alignment, and reusability.
Experience designing taxonomies, ontologies, metadata models, and content schemas for enterprise content organization, retrieval, analytics, and governance.
Proficiency in Python and relevant data science and NLP libraries such as pandas, NumPy, scikit-learn, spaCy, NLTK, transformers, sentence-transformers, and related frameworks.
Strong SQL skills and familiarity with data engineering concepts supporting text-centric workflows, corpus management, feature generation, and integration of structured and unstructured data sources.
Experience with large language models, prompt design, response evaluation, retrieval-augmented generation concepts, human-in-the-loop review, and responsible AI practices in enterprise settings.
Demonstrated ability to operate effectively in a hands-on leadership role, balancing strategic direction, stakeholder engagement, and direct execution.
Ability to work effectively across technical, product, control, risk, and commercial business domains.
Effective communication, editorial judgment, and stakeholder management skills.
High proficiency in Excel, PowerPoint, and Word.

Nice To Haves

Master’s degree in data science, computer science, computational linguistics, information science, applied mathematics, knowledge systems, business analytics, or a related technical field.
10+ years of relevant work experience.
Asset Servicing industry knowledge and experience.
Experience in Deal Management, controls architecture, product management, proposal management, sales enablement, due diligence content, or consulting environments.

Responsibilities

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 client-facing messaging.
Support AI-enabled drafting workflows through retrieval-augmented generation concepts, prompt design, response evaluation, and human-in-the-loop review approaches aligned with responsible AI practices.
Strategic Partnership and Execution: Partner with sales, product, solutions, deal management, controls, and subject matter experts to improve the sourcing, validation, prioritization, maintenance, and reuse of high-value content.
Reduce redundant SME outreach by identifying content gaps, extracting reusable knowledge from expert contributions, and converting that knowledge into governed response assets.
Lead and execute high-impact initiatives across knowledge engineering, NLP, retrieval, and AI-enabled content optimization, from problem definition through delivery.
Define project scope, milestones, deliverables, and operating cadence for strategic workstreams.
Translate analytical findings into actionable recommendations for business leaders and stakeholders.
Innovation, Measurement, and Business Impact: Advance the use of AI, NLP, language quality analytics, and content intelligence to support proposal excellence and sales enablement across Asset Servicing.
Analyze workflow bottlenecks, content usage patterns, response quality, content freshness, expert dependency, and operational inefficiencies to improve proposal cycle times and first-draft effectiveness.
Define and apply performance metrics such as reuse rates, answer acceptance, first-draft quality, manual edit rates, SME touch frequency, and cycle-time reduction.
Support a “One Asset Servicing” and “One BNY” approach through a unified, AI-enabled content strategy.
Identify opportunities to improve the proposal development lifecycle through innovations in knowledge engineering, enterprise retrieval, and language AI.