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Senior Vice President of Data Science & Advanced AnalyticsConfidential • New York, New York, United States
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Senior Vice President of Data Science & Advanced Analytics

Confidential
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Postuler Maintenant

À propos

Senior Vice President of Data Science & Advanced Analytics
About the Company
A nationally recognized financial institution seeking enterprise-wide AI and advanced analytics leadership.
Industry Banking
Type Privately Held
About the Role
The Company is in search of a Senior Vice President of Data Science and Advanced Analytics to spearhead its enterprise-wide AI, machine learning, and advanced analytics initiatives. The successful candidate will be a hands-on executive with a focus on driving the strategy, development, and operationalization of data-driven solutions that enhance efficiency, strengthen risk management, improve customer experience, and create new revenue opportunities. This role demands a leader with deep technical expertise and a proven track record in delivering production-grade analytics within regulated environments. Key responsibilities include leading the design, development, and deployment of AI and advanced analytics solutions across various domains, driving end-to-end delivery, and building scalable, production-ready data science capabilities. The SVP will also be responsible for integrating models into enterprise systems, overseeing model governance, and establishing best practices for MLOps and CI/CD automation.
Applicants for the Senior Vice President position at the company should have a minimum of 10 years' of hands-on experience in data science, advanced analytics, AI/ML engineering, or quantitative modeling, with a strong background in regulated industries. The role requires a candidate with a proven ability to deliver production AI and analytics solutions that have a measurable business impact, particularly within the financial services sector. Deep expertise in a range of technical tools and frameworks is essential, including Python, SQL, machine learning frameworks, and distributed data processing. The ideal candidate will also have experience in implementing scalable MLOps frameworks, CI/CD automation, and a strong understanding of model risk management and regulatory expectations. A Bachelor's degree in a quantitative field is required, along with excellent communication, stakeholder management skills, and the ability to influence senior executives.
Travel Percent Less than 10%
Functions
Data Management/Analytics
  • New York, New York, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

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