Senior VP, Data Science Manager

Salary: $36000 - 65000 per year Requirements: 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: 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