Corporate Vice President, Data Scientist - Advanced Analytics

New York Life•Tampa, FL

1d•Hybrid

About The Position

As a Corporate Vice President, Data Scientist on the Advanced Analytics team, you will serve as a strategic thought partner to business leaders across New York Life Direct, driving high-impact initiatives through advanced analytics, machine learning, and data-driven decision making. You will operate as a subject matter expert, shaping the direction of data science applications across marketing, underwriting, actuarial, operations, and fraud prevention. In this role, you will lead the design and development of innovative, scalable solutions, proactively identify opportunities to influence business strategy, and help define the future of data science within the organization. You will work across functions to translate complex business challenges into analytical frameworks, guide solution development, and ensure insights are effectively embedded into decision processes to deliver measurable business value.

Requirements

Advanced academic training in a quantitative field such as Data Science, Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related discipline.
Significant experience applying data science in complex business environments.
Deep technical expertise in programming languages such as Python or R.
Strong proficiency in SQL.
Robust foundation in statistical modeling, machine learning, and experimental design.
Ability to independently lead large-scale, ambiguous initiatives.
Ability to translate complex data into actionable insights that influence strategic decisions.
Experience working with large, real-world datasets.
Experience deploying models in production environments.
Effective communicator who can influence stakeholders at all levels.
Track record of mentoring others, shaping best practices, and driving continuous improvement.
Master’s or PhD in quantitative fields such as Data Science, Statistics, Applied Math, Economics, Computer Science, Engineering, or related disciplines.
5+ years of experience applying data science in a business setting with a Master’s (3+ years for PhD graduates).
Strong programming skills in Python and/or R, plus experience with SQL.
Strong foundation in statistical analysis, machine learning and experimental design.
Experience working with real-world datasets and translating ambiguous business questions into analytical solutions.
Strong communication skills, including the ability to explain technical concepts clearly and influence decisions across a range of audiences.
Ability to work independently, manage multiple priorities, and drive work forward in a collaborative environment.

Nice To Haves

Experience ideally within insurance, financial services, or direct-to-consumer contexts.
Familiarity with cloud-based platforms (e.g., AWS, SageMaker).
Familiarity with model lifecycle management.
Interest in emerging areas such as generative AI.
Experience in insurance, financial services, or direct-to-consumer environments.
Familiarity with cloud-based tools and platforms (e.g., AWS, Sagemaker).
Understanding of model lifecycle management.
Experience evaluating or applying generative AI in business settings.

Responsibilities

Partner closely with senior business stakeholders to identify, prioritize, and shape data science initiatives that align with strategic objectives, proactively uncovering opportunities where advanced analytics can drive meaningful business impact.
Lead complex, high-visibility data science projects end to end, from framing ambiguous problems and defining analytical approaches to developing, deploying, and monitoring scalable machine learning models and data products.
Leverage deep expertise in statistical methods, machine learning, and experimentation to solve unique and complex problems, often requiring innovative and conceptual thinking beyond existing approaches.
Influence decision-making by communicating insights and recommendations clearly to both technical and non-technical audiences, including senior leadership.
Collaborate with engineering and technology teams to operationalize solutions, establish best practices, and enhance model lifecycle management.
Mentor other data scientists, elevate technical standards, and contribute to the development of the team’s capabilities, while staying at the forefront of emerging trends and identifying ways to apply new techniques to advance business outcomes.