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Job Description
#### Requisition ID
94103#### Department
Foundational Business Transformation Ofc#### Job Function
Foundational Business Transformation Ofc#### Location
New York,New York,United States#### Role Location Designation
Hybrid \- 3 days per week Location Designation: Hybrid \- 3 days per week
The Vice President, Data \& AI – Foundational Business is a senior executive accountable for defining, co\-owning, and helping deliver the Data \& AI strategy for the Foundational Business. This role is responsible leveraging Data and AI as key drivers of the overall business transformation through data\-driven and AI\-enabled capability identification, defining the value creation, supporting operational impact, and financial performance working closely with business and technology delivery leaders.
The VP serves as a key leader in the Transformation Office and is a strategic advisor to executive leadership, shaping how data and AI are leveraged to drive growth, efficiency, and competitive advantage. This role has owns the Data \& AI portfolio for the Foundational Business, including strategy, execution, governance, and adoption.
Key Responsibilities
Strategy \& Transformation
- Define and own the Data \& AI strategy aligned to enterprise and Foundational Business priorities. Establish long\-term roadmaps and investment priorities for Data \& AI capabilities
- Convert the overall strategy to prioritized and actionable implementation opportunities with varying degrees of complexities i.e., business level ‘big bets’, AI is enabler to on going products/ initiatives etc.,
- Develop impact metrics and business cases aligned to AI and design changes required in talent and capabilities as an outcome of AI solutions
- Advise senior executives and influence enterprise strategy through data\-driven insights and innovation
Execution \& Delivery
- Accountable for end\-to\-end delivery of Data \& AI initiatives, ensuring measurable business outcomes and value realization in partnership with Technology leaders
- Lead prioritization and execution of a portfolio of AI and data initiatives, balancing speed, scale, and risk
- Ensure successful adoption and scaling of AI solutions across the business
Data \& Governance
- Partner with Tech Data leader to define and implement enterprise\-grade data governance frameworks across value streams, including data quality, privacy, security, and compliance
- Oversee the development and scaling of data products and platforms required to enable AI capabilities working closely with Technology leaders
- Establish standards for responsible AI and data usage across the organization
Financial Accountability
- Own and manage a Data \& AI investment portfolio, including budgeting, prioritization, and resource allocation
- Accountable for ROI and financial performance of Data \& AI initiatives
- Align investments with business strategy and transformation goals
Leadership \& Organization
- Lead and scale a multi\-disciplinary organization to become a AI first leading insurance organization
- Develop and mentor senior leaders, building organizational capability and succession pipelines
- Drive a culture of innovation, accountability, and continuous improvement
- Operate effectively in a matrixed environment, leading through both direct and indirect teams
Cross\-Functional \& External Influence
- Act as the primary interface between business and technology organizations
- Drive alignment across business units, technology, and transformation teams
- Represent the organization externally with partners, vendors, and industry forums
- Build and manage strategic partnerships to accelerate Data \& AI capabilities
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Business Administration, or related field; MBA preferred
- 12\+ years of progressive leadership experience in data, AI, analytics, and business transformation
- Proven track record of leading large\-scale, enterprise Data \& AI initiatives
- Strong understanding of business strategy, financial management, and operating models
- Deep expertise in data platforms, AI/ML technologies, and governance frameworks
- Exceptional ability to influence and communicate with executive stakeholders
Key Competencies
- Strategic Leadership
- Enterprise Thinking
- Innovation \& Transformation
- Executive Influence
- Financial Acumen
- Organizational Leadership
- Change Management
Pay Transparency
Salary Range: $250,000 \- $300,000
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180\-year legacy of purpose and integrity fuels our future. As we evolve into a more technology\-, data\-, and AI\-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem\-solving, and purpose\-driven innovation. Here, you’ll find the rare balance of long\-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.Click hereto discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of *Fortune’s* World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Visit our LinkedIn to see how our employees and agents are leading the industry and impacting communities.
Visit our Newsroom to learn more about how our company is constantly evolving to meet our clients' and employees’ needs.
Job Requisition ID: 94103
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At New York Life, 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. This role's midpoint ($275K) sits 49% above the category median. Disclosed range: $250K to $300K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
New York Life AI Hiring
New York Life has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $211K - $300K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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