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About This Role
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.
Key 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.
Qualifications:
Required
- 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.
Preferred
- 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BNY, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BNY AI Hiring
BNY has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Boston, MA, US, Pittsburgh, PA, US.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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