Director of Data Science (Generative AI)

Key Skills

Data science techniques, Generative AI, IT, Leadership Skill, Natural Language Processing (NLP), Python Programming, R Programming, SPARK Programming, SQL

Job Description

This role will be the key data science leader for building our generative AI practice, in collaboration with our head of ML Ops. It initially reports directly to Glenn and may later be integrated in the data science area with the largest use cases. The person in the role is expected to have prior experience with creating generative AI solutions and will need to attract others with that skills set. NYL’s CEO has expressed that he wants the company to be a leader in generative AI in our industry. Hence, this leader has to opportunity to help build out that vision and with it their responsibility. Since not every data science project may require generative AI, expert-level familiarity with regular statistical predictive modeling methodologies and practice is also essential.

Responsibilities

1. Builds and leads a generative AI practice within CDSAi. This includes use case development and associated stakeholder management in various business areas. It also means talent development (both recruiting new talent and training existing data scientists), infrastructure and tooling (in collaboration with ML Ops and IT), education of stakeholders and community, vendor selection/collaboration and governance (in collaboration with model governance team and Legal).
2. Managing various stakeholders during solution design and project execution to successfully create solutions and deploy them into full production. Organizational effectiveness (within a complex company like NYL), storytelling and product branding are as important as technical leadership.
3. Utilizes advanced statistical/AI techniques to create high-performing predictive models and other solutions to address business objectives and client needs. Tests new statistical and machine learning analysis methods, software and data sources for continual improvement of quantitative solutions.
4. Implements analytical models into production by collaborating with technology and ML Ops teams. Utilizes data visualization tools for model testing, modeling results and data patterns exhibition. Design performance metrics for model selection and performance monitoring.
5. Manages, grows, and retains a strong team of highly skilled data scientists. Including goal setting, performance evaluation, effective resource allocation and career/skill development, hiring and training.
6. Explores, designs, develops and gains buy-in on new valuable data science use cases. Creates a strategy and a project pipeline. Given the size and complexity of New York Life, there is ample opportunity for that. New use case development can lead to further growth of the team under this role.
7. Works closely with the business areas, IT, Legal, Government relations and several other groups in designing, building, and implementing these solutions.
8. Evangelizes the use of data-based decision making and Analytics within New York Life.
9. Proactively and effectively communicates in various verbal and written formats with internal stakeholders on product design, data specification, model implementations, with partners on collaboration ideas and specifics, with internal clients and stakeholders on project/test results, opportunities, questions. Resolves problems and removes obstacles to timely and high-quality project completion.
10. Works collaboratively with project and product managers within CDSAi and on other teams.
11. Supports internal events, expos, lunch & learns, etc. with displays and presentations.
12. Follows industry trends in insurance and related data/analytics processes and businesses. Functions as the analytics expert in meetings with other internal areas and external vendors. Contributes ideas and actively participates in proof-of-concept tests of new processes and technologies.
13. Stays up to date on existing and proposed legislation and regulation (on federal and state level) that impact AI in underwriting. Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
14. Travels to events and vendor meetings as needed (< 5%).

Required qualifications

1. Technical expertise

• Graduate-level degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or similar.
• 8+ years of experience with predictive analytics using large and complex datasets.
• Substantial expertise in both parametric statistical modeling techniques (linear regression, GLM, survival analysis, time series, etc.) and non-parametric techniques (GBM, NN, NLP).
• Expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.), validation (holdouts, CV, bootstrap) and model performance measures (may need to create new ones).
• Substantial prior programming experience in languages such as R, Python, SPARK, SQL. Comfort with professional software development process and GitHub.
• Demonstrated expertise in deploying real-time models into production environments. This includes production-ready code, containerizing models, testing, and integration into business processes.
• Detailed recent NLP experience (basic techniques, large language models, transformers, etc.)
• 2+ years of experience with generative AI solutions (both development and deployment/integration). Ideally on development this would include training, fine-tuning and prompt engineering.
• Experience with disparate impact testing vs. protected classes of statistical models is a plus.

1. Leadership, business and other non-technical

• 3+ years of direct people management experience, including growing a technical team and recruiting (and retaining) talent. Proven ability to effectively manage own and associates’ time while multitasking between multiple, time-sensitive projects and competing priorities in a dynamic business environment while maintaining strong, productive relationships with internal stakeholders and external partners. Ability to provide technical guidance to direct reports.
• Demonstrated organizational effectiveness on substantial projects that involved significant cultural change at a large company.
• Strong verbal and written communications skills, listening and teamwork skills, and effective presentation skills. This is essential since you will have a lot of exposure to different internal groups (Business functions, Data, IT, Legal, Government Relations, etc.) as well as third-party data vendors and consultants.
• Demonstrated success in creating measurable business benefit from analytics while interacting with many stakeholders in a complex organization.
• Demonstrated experience in strategic and analytical leadership. Executive presence on high-level meetings.
• Consumer financial services experience is a plus.