This role will be the key data science leader for building our generative AI practice, in collaboration with our head of ML Ops. The person in this 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
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:
Director of Data Science (Generative AI) This role will be the key data science leader for building our generative AI practice, in collaboration with our head of ML Ops. The person in this 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 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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