Lead Data Scientist (Generative AI), Corporate Vice President

Key Skills

Aartificial intelligence, Apache Hadoop, AWS, Data science techniques, Effective communication skills, IT, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Python Programming, R Programming, SPARK Programming, SQL

Job Description

Responsibilities

1. Supports and helps build a generative AI practice within CDSAi. This includes supporting use case development and associated stakeholder management in various business areas. It also means influencing infrastructure and tooling (in collaboration with ML Ops and IT), education of stakeholders and community, and governance (in collaboration with model governance team and Legal).
2. Managing various stakeholders at the same level during solution design and project execution to successfully create solutions and deploy them into full production.
3. Independently leads and contributes to data analysis and modeling projects from project/sample design, business review meetings with internal and external clients deriving requirements/deliverables, reception and processing of data, performing analyses and modeling to final reports/presentations, communication of results and implementation support.
4. Demonstrates to internal and external stakeholders how analytics can be implemented to maximize business benefits. Provides technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals.
5. 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.
6. 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.
7. Utilizes data wrangling/data matching/ETL techniques while programming in several languages to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets. Deploys analytical solution in production systems.
8. Works closely with the business areas, IT, Legal, Government relations and several other groups in designing, building, and implementing these solutions.
9. Evangelizes the use of data-based decision making and Analytics within New York Life.
10. 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.
11. Works collaboratively with project and product managers within CDSAi and on other teams.
12. Supports internal events, expos, lunch & learns, etc. with displays and presentations.
13. 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.
14. 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.
15. Travels to events and vendor meetings as needed (< 5%).

Required qualifications

• Graduate-level degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or similar.
• 4+ 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 this would include training, fine-tuning and prompt engineering.
• Experience with disparate impact testing vs. protected classes of statistical models is a plus.
• 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.
• Experience with insurance or consumer financial data is a plus.