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); 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; 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
Requirements:
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)
Text:
Corporate Vice President - Director of Data Science (Generative AI) 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); 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; 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 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)
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