Artificial Intelligence & Data

Leadership in New York Life’s Artificial Intelligence & Data organization combines technical depth with a strong focus on turning AI into practical business results. The company recently appointed a Chief AI Officer to lead enterprise AI strategy, platforms and execution, while the broader Technology, Data, AI & Ventures organization is overseen by the CIO. Within AI&D, leaders are responsible not only for setting technical direction but also for developing talent, establishing responsible-AI standards and connecting engineering and data science work to measurable business outcomes.

• AI&D now has dedicated executive leadership for AI: Chief AI Officer Zhen Zhao leads New York Life’s AI strategy, platforms and execution, including advanced AI, agentic AI and machine learning. He joined with previous experience leading global teams across AI platforms, governance, machine learning and large-language-model operations.
• Leaders are expected to stay close to the technical work: Current AI&D director positions are explicitly hands-on. Leaders can be responsible for designing and shipping AI solutions themselves while also shaping technical strategy and managing or mentoring growing teams. That creates a structure where technical leadership is expected to understand how solutions actually move from experimentation into production.
• Mentoring technical talent is part of the leadership role: AI Engineering leaders are specifically responsible for providing mentorship to data scientists and AI engineers and fostering rapid experimentation, rigorous evaluation and continuous learning. Senior technical employees are similarly expected to guide other engineers and raise technical standards across the organization.
• Leadership connects AI projects directly to business outcomes: AI&D leaders work with business, product, technology and data teams from the initial identification of an opportunity through deployment and adoption. Success is measured not simply by whether a model works, but whether the resulting AI solution delivers measurable value and is actually used by the business.
• Responsible AI is built into technical direction: Leaders establish standards around model evaluation, testing, monitoring, safety guardrails and responsible AI. New York Life’s broader AI leadership has also emphasized applying AI thoughtfully while preserving human connection, particularly in how agents and advisors serve clients.
• AI&D leadership spans several specialties rather than one centralized discipline: The organization has built dedicated leadership across data, data science and AI/data product management. That structure is designed to bring together trustworthy data, AI development and product delivery so technical teams can work toward shared enterprise priorities.
• External signals:
  • An AI engineer specifically praises the team’s leadership: A Glassdoor review from a New York Life AI engineer gave the company 5/5 and highlighted a “Great community and leaders,” along with excitement about future technologies. (Glassdoor)
  • Data scientists have also called out their direct managers positively: A New York-based data scientist gave New York Life a 5/5 Indeed review and specifically noted having a good manager and team lead, while describing it as a good place to work and learn. (Indeed)
  • The broader New York workforce gives leadership strong marks: New York Life’s New York employees rate Leadership 82/100 and Managers 80/100 on Comparably, with both scores placing the company in the top 5% of similarly sized companies on the platform. (Comparably)

Bottom line: AI&D leadership at New York Life combines hands-on technical expertise with mentorship, responsible AI and a clear expectation that technology should solve real business problems. Employees can work with leaders who are helping set enterprise AI standards while still staying closely involved in how products are designed, built and put into use.

Work-life balance in New York Life’s Artificial Intelligence & Data organization combines a fast-moving technical environment with structured flexibility. New York Life describes AI&D as “fast-paced and supportive” while also listing flexible work options as a team perk. Most current New York-based AI&D positions use a hybrid schedule of three days per week in the office, giving employees regular time with technical and business partners while preserving work-from-home days.

• Three office days is the standard for many AI&D roles: Current openings for principal AI engineers, AI engineering directors, data scientists and model-validation and AI-governance professionals are designated hybrid employees at three days per week. The pattern extends across different specialties and seniority levels within AI&D.
• Flexibility exists because the team collaborates heavily across functions: AI&D employees work closely with data scientists, engineers, product managers, technology teams and business stakeholders throughout the lifecycle of AI products. The hybrid structure therefore balances focused individual technical work with recurring opportunities for in-person collaboration.
• The pace can be demanding when AI products move toward production: AI&D roles cover model development, validation, deployment, monitoring and iteration rather than stopping at experimentation. Senior engineers and technical leaders are also expected to help establish enterprise AI standards while delivering solutions, so workload can increase around launches, complex technical problems and production deadlines.
• New York Life specifically promotes flexibility as an AI&D benefit: The AI&D careers page lists both a “fast-paced and supportive work environment” and “flexible work options” among the advantages of joining the organization. That combination suggests the team is designed for employees who want challenging technical work without giving up all control over where that work happens.
• AI employees can also benefit from reduced summer hours: Current AI engineering postings include New York Life’s Summer Flex Time benefit, which provides reduced working hours during the summer months. The same postings highlight the hybrid environment as a resource intended to support greater work-life balance.
• Family-support resources add another layer of flexibility: Current AI roles advertise paid new-parent benefits, fertility assistance, backup childcare and free virtual tutoring. These benefits can be particularly useful when employees need to balance technically demanding work with responsibilities outside the office.
• External signals:
  • A data scientist specifically calls work-life balance a strength: A New York Life data scientist gave the company a 5/5 Glassdoor review and listed “Good work and life balance” as a positive of the job. (Glassdoor)
  • New York employees report a generally manageable workday: Comparably says employees in New York are satisfied with their work-life balance and report working an average of about eight hours per day at what the platform describes as a comfortably fast pace. (Comparably)
  • Flexibility is one of the recurring positives in employee feedback: Indeed gives New York Life a 3.6/5 work-life balance rating and identifies time and location flexibility among the things employees commonly appreciate. These ratings cover the broader company rather than AI&D specifically. (Indeed)

Bottom line: Work-life balance in New York Life AI&D combines a fairly consistent three-day hybrid schedule with the faster pace that comes from building and deploying enterprise AI. Employees get meaningful flexibility, summer reduced-hours programs and family-support resources, while still needing to accommodate periods of heavier work when technical projects move toward production. 

Culture in New York Life’s Artificial Intelligence & Data organization centers on experimentation, collaboration and applying emerging technology to real business problems. New York Life describes the team as fast-paced and supportive, with challenging projects, opportunities to learn and an emphasis on creating measurable value. AI&D employees work across data science, engineering, product and governance, so the culture favors people who are comfortable sharing ideas, learning from other specialties and testing new approaches while maintaining the standards required in financial services. 

• Experimentation is encouraged, but projects need a practical purpose: AI&D works across traditional machine learning, generative AI and agentic AI, with employees contributing throughout the model lifecycle from exploration and development to deployment and monitoring. The goal is not simply to experiment with new technology, but to translate it into improvements for clients, agents and employees.
• Collaboration happens across technical specialties and business teams: Data scientists work alongside engineers, product professionals, technology teams and business stakeholders. New York Life’s 2024 Data+Analytics+AI Expo illustrated that approach particularly well: many projects were co-presented by business employees and analytics or AI teams, highlighting how technical specialists and business partners develop solutions together.
• Employees are encouraged to share what they are building: AI&D hosts large internal forums where employees demonstrate projects and learn from one another. The 2024 Data+Analytics+AI Expo featured more than 120 employee presenters across 39 booths, with more than 700 people participating in person or virtually. Projects ranged from GenAI knowledge tools to underwriting modernization and predictive marketing models.
• Learning new technology is part of the culture: AI&D operates within a company making a significant push to increase AI fluency. In 2026, New York Life’s Ignite AI Learning Week included hands-on labs developed with Microsoft and OpenAI and drew nearly 3,000 registrations for those sessions. More than nine in 10 employees who provided feedback rated the sessions as valuable and relevant.
• Responsible AI carries real weight: Working with AI in insurance requires employees to think about data quality, governance, cybersecurity, model performance and how technology affects people. Current data science roles explicitly include responsible-AI principles, while company leadership emphasizes combining new technology with trust and human guidance rather than treating AI as a replacement for those relationships.
• The team creates opportunities for employees to learn from each other: Internal expos, hackathons and other innovation programs provide forums for employees to experiment outside their usual work and see how other teams solve problems. New York Life’s employee hackathons specifically encourage cross-functional teams to apply AI and other technologies to real company challenges.
• External signals:
  • An AI engineer specifically praises the team community: A New York Life AI engineer gave the company a 5/5 Glassdoor review, highlighting the “Great community and leaders” and enthusiasm about future technologies. (Glassdoor)
  • Data-science employees give their work environment strong marks: Indeed lists both Data Scientist and Lead Data Scientist at 5/5 based on the available role-specific reviews. A lead data scientist highlighted the company’s focus on humanity and how it treats employees and customers. (Indeed)
  • Data employees describe a supportive working environment: A New York-based data analytics intern said colleagues were kind and supportive and highlighted the sense of fulfillment that came from the work. (Indeed)

Bottom line: AI&D at New York Life combines a collaborative, learning-oriented culture with the rigor of building AI for a highly regulated business. The strongest fit is likely someone who wants to experiment with emerging technology, share ideas across disciplines and turn that experimentation into responsible tools that people actually use. 

Career growth in New York Life’s Artificial Intelligence & Data organization combines hands-on technical development with formal learning, mentorship and opportunities to take on increasingly complex AI work. Current roles range from associate data scientists building foundational skills to principal engineers and directors shaping enterprise AI strategy, suggesting paths for employees who want to deepen their technical expertise, move into leadership or broaden into areas such as AI engineering, product and governance. 

• Employees can build from foundational AI skills into more complex work: New York Life’s associate data scientist role is specifically designed around developing core technical skills and learning enterprise AI practices. Employees gain experience across data exploration, model training and validation, deployment, monitoring and responsible AI while working with data scientists, engineers and product and technology partners.
• The organization offers visible technical progression: Current AI&D openings span associate and senior data science positions through principal AI engineer and director roles. At the senior end, employees are expected to own increasingly complex solutions, influence enterprise standards and mentor other engineers and data scientists, creating a path where growth can come through technical leadership as well as people management.
• AI&D employees have access to specialized learning programs: New York Life’s career-development resources include a Data Science Academy designed to build deeper expertise. Technologists also have access to a learning stipend that employees and their managers can use for external skills-development programs selected around individual needs. Corporate employees average 13 hours of training, with more than 250,000 digital and live courses completed annually across the company.
• Keeping up with emerging AI tools is built into company learning: New York Life’s 2026 Ignite AI Learning Week included hands-on labs developed with Microsoft and OpenAI, employee and leadership panels and practical AI use cases. The hands-on sessions drew nearly 3,000 registrations, and more than nine in 10 employees who provided feedback said the sessions were valuable and relevant.
• Senior technical employees are expected to develop others: Principal AI engineering roles explicitly include mentoring data scientists and AI engineers while fostering rapid experimentation, rigorous evaluation and continuous learning. That gives less-experienced team members opportunities to learn directly from colleagues who are designing and deploying enterprise AI systems.
• Career growth can also come from moving into new disciplines: AI&D includes data science, AI engineering, MLOps, product management, data strategy and AI governance. New York Life’s broader Internal Mobility Program adds career counseling, resume and interview coaching and guidance for lateral moves, making it possible for employees to explore paths beyond the specialty where they started. The program had helped 401 participants move into new internal roles as of August 2023.
• AI&D employees are encouraged to broaden their perspective, not just their technical skills: In a New York Life career discussion, a data science director described growth as becoming comfortable with unfamiliar situations, while other technology and data leaders discussed how moving between roles helped them connect technical knowledge with broader business value.
• External signals:
  • Data scientists specifically describe New York Life as a place to learn: A New York-based data scientist gave the company a 5/5 Indeed review and called it a “Great place to work and learn,” while also praising their manager, team lead and teammates. (Indeed)
  • AI engineers highlight both leadership and the opportunity to work with emerging technology: An AI engineer review on Glassdoor gave New York Life 5/5 and praised the community and leaders while expressing enthusiasm about future technologies. (Glassdoor)
  • The broader New York workforce shows strong retention sentiment: New York Life’s New York employees give Retention an 81/100 score on Comparably, placing it in the top 5% of similarly sized companies measured by the platform. (Comparably)

Bottom line: Career growth in New York Life AI&D can mean progressing into more advanced data science or AI engineering work, developing into a technical or people leader, or moving across specialties such as product, MLOps and governance. Formal learning, mentorship and exposure to new AI technologies give employees multiple ways to keep building their skills as the organization evolves.

New York Life gives its Artificial Intelligence & Data team several ways to keep building technical skills, from a dedicated Data Science Academy and AI learning paths to external training, internal knowledge-sharing and hands-on events. The company’s broader learning infrastructure is complemented by AI&D-specific programs that help employees stay current as machine learning, generative AI and enterprise data practices evolve. 

• The Data Science Academy provides structured technical development: New York Life identifies its Data Science Academy as one of its formal programs for developing deep expertise. Earlier versions of the academy have included learning paths in business analytics, data science, machine learning, Python and SQL, creating structured ways for employees to strengthen both foundational and advanced data skills.
• AI employees can use a dedicated GenAI learning path: New York Life added a GenAI learning path to its Learning Exchange that connects employees with training, resources and communities covering GenAI practices, disciplines and governance. That is particularly relevant for AI&D employees who need to understand both how newer AI tools work and how to apply them responsibly inside a financial-services company.
• Hands-on AI training includes work with major technology partners: During Ignite AI Learning Week in April 2026, employees participated in practical “Power Labs” developed with Microsoft and OpenAI, using tools including Copilot and ChatGPT Enterprise. The labs attracted nearly 3,000 registrations, and more than nine in 10 employees who provided feedback rated the sessions as valuable and relevant.
• AI&D has its own knowledge-sharing forums: The team hosts an AI & Data “Lunch & Learn” series where speakers share expertise and insights around AI and data. New York Life says these sessions are intended to improve technical literacy and help employees understand how to apply AI effectively and responsibly.
• Technologists can pursue outside learning tailored to their skills: New York Life offers technologists a learning stipend that employees and managers can use for individually selected external skills-development programs. That gives AI&D professionals another way to pursue specialized training as their role or technology stack changes.
• Internal events let employees learn from real AI projects: The Data+Analytics+AI Expo gives employees a chance to see how other teams are using AI, analytics and data across New York Life. The 2024 event included more than 120 employee presenters across 39 booths and covered topics including GenAI, underwriting modernization and predictive modeling. The 2025 Innovation Summit expanded that model with sessions from organizations including Anthropic, Microsoft, OpenAI, dbt Labs and Tableau.
• Learning also happens through experimentation: New York Life runs employee hackathons where cross-functional teams apply AI and other technologies to real business challenges. AI in Action Day similarly grew out of a three-month Ignite AI program focused on building employees’ AI mindset, toolset and skillset, with selected employees demonstrating custom GPTs, Copilot agents and other practical applications.
• Day-to-day AI&D roles are designed to build skills on the job: Associate data scientists are expected to develop core technical skills while learning model lifecycle management, responsible AI and New York Life’s technology environment. They work alongside data scientists, engineers, product teams and technology partners, creating opportunities to learn from specialists in adjacent disciplines while delivering actual AI projects.
• External signals:
  • Data scientists specifically describe the company as a place to learn: A New York-based data scientist gave New York Life 5/5 on Indeed and described it as a “Great place to work and learn,” while also praising their manager, team lead and teammates. (Indeed)
  • Recent employee feedback also recognizes New York Life’s training resources: A Glassdoor review from a senior associate product owner listed a “Great training program” among the positives of working at the company. This reflects the broader corporate experience rather than AI&D specifically. (Glassdoor)

Bottom line: Training in New York Life AI&D goes beyond a standard course catalog. Employees can build technical depth through the Data Science Academy and GenAI learning paths, learn directly from peers and outside technology partners, experiment through hackathons and showcases, and use external learning funds to develop skills that match where AI and data technology are heading.