Senior Associate (Senior Data Scientist)

New York Life Insurance Company Senior Associate (Senior Data Scientist) New York , New York Apply Now Employer: New York Life Insurance CompanyJob Title: Senior Associate (Senior Data Scientist)Location: New York, NY Offered Wage: $163,238/yearDuties: As part of the company's Center for Data Science and Artificial Intelligence (CDSAi), leads and contributes to data analysis and modeling projects from project sample design, and business review meetings with internal and external clients. Determines requirements, deliverables, reception, and data processing. Performs data analyses and modeling to final reports and presentations, communicates results. Demonstrates to internal and external stakeholders how analytics can be implemented to maximize business benefits. Provides technical support, including strategic consulting, needs assessments, project scoping, and preparing and presenting analytical proposals. Creates high-performing predictive models and creative analyses leveraging advanced statistical and machine-learning techniques to address business objectives and client needs. Tests new statistical and machine-learning analysis methods and software and data sources for continual improvement of quantitative solutions. Implements analytical models into production by collaborating with internal Technology and Operations teams. Leverages data visualization tools for model testing, modeling results, and exhibiting data patterns. Designs performance metrics for model selection and performance monitoring. Performs data wrangling and data matching leveraging extract, transfer, load (ETL) techniques. Programs applications in multiple languages to explore a variety of data sources, gain data expertise, perform summary analyses, and prepare modeling datasets. Deploys analytical solutions in the Production systems. Communicates with internal stakeholders concerning product design, data specifications, and model implementations and with partners concerning collaboration ideas. Creates project milestone plans to ensure projects are completed on time and within budget. Follows industry trends in insurance and related data analytics processes and businesses. Participates in proof-of-concept tests for new data, software, and technologies. Ensures compliance with regulatory and privacy requirements during the design and implementation of modeling and analysis projects.Education & Experience Requirements:Master's degree in Statistics, Computer Science, Mathematics, Machine-Learning or related quantitative field (willing to accept foreign education equivalent) plus three (3) years of experience coding and performing predictive modeling using large and complex datasets for the consumer finance or Insurance industry.Or, alternatively:Bachelor's degree in Statistics, Computer Science, Mathematics, Machine-Learning or related quantitative field (willing to accept foreign education equivalent) and five (5) years of experience coding and performing predictive modeling using large and complex datasets for the consumer finance or Insurance industry.Required Skills:Experience must include 2 years in each of the following skills:(1) Developing and deploying time-to-event survival models into production leveraging proportional hazard models, Random Survival Forest or accelerated failure time in the consumer finance or insurance industry; (2) Programming production-ready code leveraging Python, R, SQL or Spark to extract and transform data from multiple data sources (using SQL, Oracle or Hadoop) for modeling data consistency management and data analysis reports; and to test, deploy, and integrate statistical models into business operation and decision-making processes; (3) Developing parametric statistical models (including linear regression, time series, and generalized linear models (GLMs) and non-parametric models (including Random Forest, XGBoost, and gradient boosting machine (GBM) tree models) in R or Python using large and complex datasets for the insurance or consumer finance industry; (4) Building and fine-tuning high-performing, robust statistical models for the consumer finance or insurance industry leveraging: regularization techniques (including Ridge, Lasso or elastic nets), variable selection techniques (including stepwise selections, weight of evidence, and information value), feature engineering (including transformation, binning, imputation, and high-level categorical reduction), validation (including holdouts, cross-validation, and bootstrapping), and proper model performance measures in R or Python; and, (5) Performing complex data visualization leveraging R or Python for exploratory data analysis and model performance illustration to formulate business needs into statistical analysis and make model solution recommendations to business partners.Apply online at: http://www.newyorklife.com/careersMinimum Salary: 163238Maximum Salary: 163238Salary Unit: Yearly#J-18808-Ljbffr