Senior VP, Data Science Manager Edit listing

Requirements
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### Must have:

### - Bachelors degree or equivalent practical experience; advanced studies in a related field are preferred. - Extensive background in data science, NLP, text analytics, knowledge engineering, knowledge management, content operations, proposal enablement, sales analytics, or a similar analytical discipline. - Strong experience handling large volumes of unstructured text, document repositories, and enterprise content libraries. - Proven expertise with NLP and language-focused machine learning methods such as semantic search, embeddings, similarity scoring, classification, clustering, duplicate detection, topic extraction, summarization, named entity recognition, and information extraction. - Experience designing AI- or analytics-driven solutions for content that requires technical accuracy, control awareness, regulatory precision, and clear client-facing messaging. - Deep understanding of language quality factors including factual consistency, clarity, readability, tone, relevance, persuasiveness, and alignment to approved messaging. - Experience building scoring, ranking, recommendation, or retrieval models for business text based on relevance, freshness, quality, specificity, strategic fit, and reuse potential. - Experience creating taxonomies, ontologies, metadata structures, and content schemas for enterprise organization, retrieval, analytics, and governance. - Proficiency in Python and data science/NLP libraries such as pandas, NumPy, scikit-learn, spaCy, NLTK, transformers, and sentence-transformers. - Strong SQL skills and familiarity with data engineering concepts supporting text workflows, corpus management, feature creation, and integration of structured and unstructured sources. - Experience with large language models, prompt design, response evaluation, retrieval-augmented generation, human-in-the-loop review, and responsible AI practices in enterprise settings. - Ability to operate effectively in a hands-on leadership role, balancing strategy, stakeholder engagement, and direct execution. - Ability to work across technical, product, control, risk, and commercial domains. - Strong communication, editorial judgment, and stakeholder management skills. - High proficiency in Excel, PowerPoint, and Word. - A masters degree in data science, computer science, computational linguistics, information science, applied mathematics, knowledge systems, business analytics, or a related technical field is preferred. - 10+ years of relevant experience is preferred. - Asset Servicing industry knowledge is preferred. - Experience in Deal Management, controls architecture, product management, proposal management, sales enablement, due diligence content, or consulting environments is preferred.

Responsibilities:
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- We will lead the transformation of our Asset Servicing proposal knowledge base to improve first-draft quality, consistency, speed, and completeness across client opportunities. - We will design scalable methods 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 approved firm materials. - We will develop approaches that organize content so it clearly, accurately, and persuasively communicates technical, operational, product, service, risk, and control information. - We will establish governance standards across taxonomy, ontology, metadata models, content schemas, lifecycle management, editorial quality, approvals, and version control. - We will integrate and normalize diverse content sources into a unified, governed, and analytically manageable ecosystem spanning structured and unstructured assets. - We will apply NLP, text analytics, machine learning, and AI methods to improve response drafting, semantic retrieval, content reuse, and language quality. - We will develop solutions using semantic search, embeddings, similarity scoring, classification, clustering, duplicate detection, topic extraction, summarization, metadata tagging, named entity recognition, and information extraction. - We will build scoring, ranking, and answer recommendation frameworks to surface the most relevant, current, high-quality, and reusable content for proposal and due diligence needs. - We will create frameworks to evaluate and improve technical explanatory content, service model descriptions, control and risk language, product capability statements, proof points, differentiators, and client-facing messaging. - We will support AI-enabled drafting workflows through retrieval-augmented generation concepts, prompt design, response evaluation, and human-in-the-loop review aligned with responsible AI principles. - We will 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. - We will reduce redundant SME outreach by identifying content gaps, extracting reusable knowledge from expert input, and converting it into governed response assets. - We will lead high-impact initiatives across knowledge engineering, NLP, retrieval, and AI-enabled content optimization from problem definition through delivery. - We will define project scope, milestones, deliverables, and operating cadence for strategic workstreams. - We will translate analytical findings into practical recommendations for business leaders and stakeholders. - We will advance the use of AI, NLP, language quality analytics, and content intelligence to support proposal excellence and sales enablement across Asset Servicing. - We will analyze workflow bottlenecks, content usage patterns, response quality, content freshness, expert dependency, and operational inefficiencies to improve cycle times and first-draft effectiveness. - We will define and apply performance metrics such as reuse rates, answer acceptance, first-draft quality, manual edit rates, SME touch frequency, and cycle-time reduction. - We will support a unified One Asset Servicing and One BNY content strategy. - We will identify opportunities to improve the proposal lifecycle through innovations in knowledge engineering, enterprise retrieval, and language AI.
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Company:
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BNY is a leading global financial services company at the center of the global financial system, influencing nearly 20% of the worlds investible assets. Our culture supports our ability to run the company better while enabling employee growth and success. We bring together advanced AI, breakthrough technology, and exceptional talent to deliver transformative solutions for clients and communities worldwide. This role is an SVP, Data Science Manager position within Asset Servicing Deal Management and Controls, based in Boston, and it focuses on building a governed, scalable content ecosystem that improves proposal and due diligence effectiveness across Asset Servicing. We are known as a destination for innovators, where bold ideas and advanced technology come together to shape the future of finance.
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