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Vice President Machine Learning Jobs in New York

Mutual of America

Vice President, AI Strategy

Manhattan, NY · On-site

$176K - $265K/yr

Collaborate with the VP of Data, ensuring the data platform efficiently supports AI, machine learning, and analytics capabilities across all departments. * Maintain authority and accountability for ...

Mutual of America

Vice President, AI Strategy

Manhattan, NY · On-site

$176.70 - $265.10/hr

Collaborate with the VP of Data to ensure the data platform efficiently supports AI, machine learning, and analytics capabilities across all departments. * Maintain authority and accountability for ...

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Vice President Machine Learning information

See New York salary details

$38.8K

$125.5K

$198.6K

How much do vice president machine learning jobs pay per year?

As of Aug 14, 2026, the average yearly pay for vice president machine learning in New York is $125,516.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,900.00 and $156,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a vice president of machine learning?

To thrive as a Vice President of Machine Learning, you need advanced expertise in machine learning, data science, and computer science, typically backed by a master's or PhD and extensive industry experience. Proficiency with platforms like TensorFlow, PyTorch, cloud computing services, and experience managing large-scale AI projects are crucial, along with a track record in leading technical teams. Exceptional leadership, strategic vision, and strong communication skills set outstanding candidates apart by enabling effective cross-functional collaboration and innovation. These skills are vital for driving organizational AI strategy, ensuring technical excellence, and delivering scalable business impact.

What does a vice president of machine learning do?

A Vice President of Machine Learning leads and oversees the strategic direction of machine learning initiatives within an organization. They manage teams of data scientists, engineers, and researchers to develop and deploy AI-driven solutions that support business goals. This role involves collaborating with other executives, setting research agendas, ensuring best practices, and staying updated with the latest advancements in the field. The VP also plays a key role in resource allocation, talent acquisition, and scaling machine learning systems across the company.

What are some common challenges faced by a vice president of machine learning when leading cross-functional teams?

A Vice President of Machine Learning often encounters challenges such as aligning diverse teams on technical priorities, managing expectations across product, engineering, and business units, and ensuring effective communication between stakeholders with varying levels of technical expertise. Balancing the need for innovation with practical business objectives and resource constraints is also a frequent challenge. Cultivating a collaborative culture and fostering ongoing professional development are key to overcoming these hurdles and driving successful outcomes.

What is the difference between Vice President Machine Learning vs Director of Machine Learning?

AspectVice President Machine LearningDirector of Machine Learning
Required CredentialsAdvanced degrees (Master's/PhD), extensive experience in MLSimilar educational background, less senior experience needed
Work EnvironmentStrategic leadership, cross-departmental collaborationProject management, team oversight
Employer & Industry UsageLarge tech firms, enterprises with AI focusTech companies, startups, research labs
Search & Comparison IntentHigh overlap in responsibilities and qualificationsRelated but more operational role

The Vice President Machine Learning typically holds a senior leadership role focused on strategic planning and cross-functional collaboration, while the Director of Machine Learning manages day-to-day projects and teams. Both roles require advanced degrees and experience in machine learning, but the VP is more involved in high-level decision-making and industry strategy.

Is vice president machine learning a high paying job?

The Vice President of Machine Learning is typically a high-level executive role with a competitive salary that reflects expertise in AI, data science, and leadership. Salaries often range from six to seven figures depending on the industry, company size, and location.

What are the most commonly searched types of Machine Learning jobs in New York?

The most popular types of Machine Learning jobs in New York are:

What job categories do people searching Vice President Machine Learning jobs in New York look for?

The top searched job categories for Vice President Machine Learning jobs in New York are:

What cities in New York are hiring for Vice President Machine Learning jobs?

Cities in New York with the most Vice President Machine Learning job openings:

Vice President Machine Learning jobs near you

Machine Learning, Vice President

Morgan Stanley

New York, NY • On-site

Full-time

Posted 21 days ago

Morgan Stanley rating

8.4

Company rating: 8.4 out of 10

Based on 155 frontline employees who took The Breakroom Quiz

30th of 150 rated financial services

Job description

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries.
As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.
The Machine Learning team in the Wealth Management (WM) Strategy & Analytics division at Morgan Stanley works on a breadth of applied AI research areas including but not limited to recommender systems, client personalization, graphical neural networks (GNNs), and natural language understanding/LLMs. We provide machine learning (ML) solutions to our internal stakeholders across all our clients channels (Advisor-led, Workplace, and Self-directed) and Product organizations (Investment Solutions, Bank) as well as functions (Marketing, Risk). Our ML scientists ideate, innovate, design, prototype, and ship ML solutions delivering delightful new experiences to 20M+ WM clients.
Responsibilities
  • Lead the design, development, and delivery of end-to-end machine learning solutions to address strategic business opportunities in Wealth Management, delivering measurable business outcomes.
  • Leverage AI/ML modeling and algorithms to deliver use cases supporting the growth plan across Client advisor and product strategy.
  • Build modeling solutions at speed and scale to solve complex business problems across large client and advisor populations.
  • Investigate, design, and create experimental prototypes focused on specific business domains and verticals.
  • Analyze large, complex data sets to quantitatively reveal underlying patterns, correlations, trends, and growth opportunities.
  • Strive to develop and experiment with state-of-the-art algorithms, including advanced machine learning, deep learning, recommender systems, and emerging AI approaches.
  • Support and enhance existing models to ensure improved performance, stability, scalability, and business impact.
  • Set up and conduct large-scale experiments, including A/B tests, to test hypotheses and drive business growth.
  • Validate machine learning models in collaboration with validation teams to ensure accuracy, reliability, explainability, and compliance with model governance standards.
  • Deploy machine learning models in production environments in collaboration with MLOps and technology teams, and monitor performance over time.
  • Participate in and lead code reviews, modeling reviews, and technical design discussions to raise engineering and modeling standards across the team.
  • Build, grow, and strengthen partnerships with business stakeholders, Marketing, Digital, Product, Risk, Legal, Compliance, Technology, and other cross-functional partners.
  • Create executive-ready presentations and analytical narratives to effectively communicate modeling results, business implications, and strategic recommendations to senior stakeholders.
  • Mentor junior data scientists and contribute to the development of team best practices, reusable modeling assets, and scalable AI/ML frameworks.

Qualifications
  • Master's degree or Ph.D. preferred in an analytical or technical field such as Computer Science, Engineering, Applied Mathematics, Physics, Statistics, Operations Research, or an equivalent quantitative discipline.
  • Minimum of 8 years of professional experience in data science, machine learning, AI, advanced analytics, or a related quantitative field.
  • Advanced knowledge of statistical and machine learning methods, particularly in modeling, classification, regression, recommender systems, clustering, deep learning, and experimental design.
  • Demonstrated hands-on experience building models at speed and scale to solve complex commercial or business problems.
  • Experience conceiving, implementing, deploying, and continually improving machine learning projects in production or production-like environments.
  • Minimum of 8 years of experience programming in SQL, Python, and/or R.
  • Proficiency in autonomously conducting applied ML research with commercial applications and translating business problems into scalable modeling solutions.
  • Strong familiarity with higher-level trends in artificial intelligence, generative AI, LLMs, and open-source AI/ML platforms.
  • Experience working with AWS, Azure, Google Cloud, or similar cloud platforms.
  • Experience with code versioning systems such as GitHub or Bitbucket, and experiment tracking systems such as MLflow or equivalent.
  • Proficiency with computer science fundamentals, including object-oriented design, data structures, and algorithmic design.
  • Strategic thinker and influencer with demonstrated leadership acumen, problem-solving skills, and ability to drive outcomes across cross-functional teams.
  • Strong communication skills with experience presenting technical concepts, modeling results, and business recommendations to senior business stakeholders.
  • Familiarity with visualization techniques and software to communicate analytical insights effectively.
  • Proficiency in English

Preferred
  • Experience with Cloud or Big Data technologies such as Azure, AWS, Google Coud, Hadoop, or an equivalent
  • Familiarity with Deep Learning frameworks (PyTorch, Tensorflow, PyTorch - Geometric, or equivalent).
  • Experience with Graphical Neural Networks, Reinforcement Learning, LLMs, Transformer based Models, or Recommender Systems is a plus.
  • Track record of publishing in peer-reviewed scientific journals

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients - helping them reach their goals. We do it in a way that's differentiated - and we've done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren't just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you'll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There's also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $115,000 and $190,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.

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