Data Science Director Jobs in Massachusetts
Data Science Director jobs in Massachusetts are among the most active in the country, concentrated in life sciences, financial services, technology, and healthcare, with demand at every level from newly promoted analytics lead through senior executive. Boston, Cambridge, and Waltham are the primary hiring hubs, where established employers like Biogen, Fidelity Investments, and Mass General Brigham consistently seek leaders who can scale data programs. The most in-demand specialties are machine learning platform leadership, clinical and biomedical analytics, and financial risk modeling. Find a role that fits below and apply directly.
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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.
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.
See All 21 Data Science Director Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find Data Science Director JobsData Science Director Jobs by City in Massachusetts
Where Massachusetts roles are concentrated, by current openings.
Data Science Director Job Market in Massachusetts
A snapshot from current Massachusetts openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Biotechnology & Pharmaceuticals
- Fintech
- Technology & Software
- Consulting & Professional Services
What Massachusetts Employers Look For
The qualifications that appear most often in data science director jobs across Massachusetts.
- Master's or doctoral degree in data science, statistics, computer science, or a related quantitative field
- Seven or more years of progressively senior experience in data science or applied machine learning roles
- Demonstrated experience leading and growing teams of data scientists and machine learning engineers
- Deep proficiency in Python, R, and cloud data platforms such as AWS, Azure, or Google Cloud
- Experience driving data strategy in regulated industries common in Massachusetts, such as life sciences or financial services
- Strong executive communication skills translating complex model outputs into business decisions for non-technical stakeholders
Data Science Director Jobs in Massachusetts: Frequently Asked Questions
How do you become a data science director in Massachusetts?
Data science director is not a state-licensed role in Massachusetts, so there is no board exam or registration required. The typical path moves through individual contributor data scientist positions into a senior or staff scientist role, then into a managing or principal position before reaching director level. Massachusetts employers in life sciences and finance strongly favor candidates with a graduate degree in a quantitative field, and industry-recognized credentials such as those from the American Statistical Association carry meaningful weight.
How much do data science directors make in Massachusetts?
Data science directors in Massachusetts earn a median of about $131,750 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $81,030 for the lowest 10% to over $206,220 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire data science directors in Massachusetts?
Employers hiring data science directors in Massachusetts right now include Novartis, BNY, and IPSEN, based on current listings on Migrate Mate as of August 2026. Massachusetts's dense concentration of biotech, asset management, and health system headquarters means director-level demand is unusually broad compared with most other states.
Which Massachusetts cities have the most data science director jobs?
Boston, Cambridge, and Waltham account for the largest share of data science director openings in Massachusetts. Boston and Cambridge anchor the market through their convergence of academic medical centers, biotech campuses, and financial firms, while Waltham and other Route 128 corridor communities host the regional headquarters and R&D operations of companies that require senior data leadership on site.
Are there remote data science director jobs in Massachusetts?
Yes, and more than most fields, because data science director work is fundamentally analytical and communicative rather than hands-on or location-bound. About 40% of data science director openings tied to Massachusetts are remote or hybrid as of August 2026, reflecting how broadly this discipline has adopted distributed work. Strategic leadership and model governance responsibilities tend to be the most portable parts of the role.
How can I get hired as a data science director in Massachusetts with little or no experience?
The most realistic path is entering through a senior individual contributor role, such as a staff data scientist or machine learning engineer, and building leadership experience from there. Large Massachusetts employers including hospitals affiliated with the Harvard Medical School system and major asset managers run rotational analytics programs and associate data scientist tracks that develop future leaders. Building a portfolio of end-to-end project work and obtaining a graduate degree in a quantitative field substantially improves candidacy for director-track roles.
Where can I find and apply to data science director jobs in Massachusetts?
You can find and apply to data science director jobs in Massachusetts on Migrate Mate, which lists current Massachusetts openings from employers actively hiring. Find roles that fit your background and apply directly to the ones that match.
See All 21 Data Science Director Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find Data Science Director Jobs