SVP, Data Science & Knowledge Engineering for Proposals

BNY; Boston (MA); On-site; USD 170,000 - 260,000; Full time

Job summary

BNY is seeking a Data Science Manager, Revenue Analytics in Asset Servicing, based in Boston. You will lead the creation of a governed content ecosystem, applying semantic search, embeddings, and NLP to improve proposal quality and efficiency.

This role blends data science leadership with knowledge engineering, enabling scalable content reuse across client opportunities and due diligence work. You will partner with sales, controls, and SME teams to minimize SME outreach, drive measurable

Qualifications

• 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.

Responsibilities

• 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.
• 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.
• Partner with sales, product, solutions, deal management, controls, and subject matter experts to improve 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.

Skills

Data science

NLP

Knowledge engineering

Python

SQL

Machine learning

Communication

Editorial judgment

Education

Bachelor’s degree or equivalent

Master’s degree (preferred)

Tools

pandas

NumPy

scikit-learn

spaCy

transformers

Job description

BNY is seeking a Data Science Manager, Revenue Analytics in Asset Servicing, based in Boston. You will lead the creation of a governed content ecosystem, applying semantic search, embeddings, and NLP to improve proposal quality and efficiency.

This role blends data science leadership with knowledge engineering, enabling scalable content reuse across client opportunities and due diligence work. You will partner with sales, controls, and SME teams to minimize SME outreach, drive measurable