Overview
Remote
On Site
USD 178,200.10 per year
Full Time
Skills
Life insurance
Data Science
Artificial intelligence
Analytical skill
Machine Learning (ML)
Data Analysis
Technical Support
Management consulting
Project scoping
Continuous improvement
Computer science
Statistical models
Data wrangling
Parallel computing
Distributed computing
Data processing
Deep learning
k-means clustering
Data
Analytics
Fraud
Marketing
Software development
Modeling
Software deployment
Design
Presentations
Creativity
Statistics
Mathematics
Finance
Python
R
SQL
SAS
Extract
transform
load
Apache Hadoop
Apache Spark
Transformation
Optimization
GRID
Testing
Management
XGBoost
Sales
Job Details
Job Requisition ID: 89245
Location Designation: Hybrid
Employer: New York Life Insurance Company
Job Title: Senior Associate, Data Scientist
Location: This position reports to the NY Life Headquarters in New York, NY but applicants may work from a Home Office from anywhere in the United States
Offered Wage: $178,200.10/year
Duties: As part of the company's Center for Data Science and Artificial Intelligence (CDSAi) corporate analytics group, applies analytical skills to work on all aspects of the life insurance value chain, ranging from risk models, fraud detection, customer behavior study, process triaging, and marketing prediction to a variety of other analytics solutions. Applies technical data, analytical, and programming skills to ingest, wrangle, and explore external and internal data to create data assets and reports. Functions as the data expert and prepares data for modeling, supports production deployment of models, and builds world-class machine-learning models to solve tangible business problems. Contributes to data analysis and modeling projects from project and sample design, business review meetings with internal and external clients to determine requirements and deliverables, and the receipt and processing of data. Performs analyses and modeling for final reports and presentations, communicates results, implements support, and 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. Leverages advanced statistical and machine-learning techniques to create high-performing predictive models and creative analyses 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. Implements analytical models into production by collaborating with internal Technology and Operation teams.
Education & Experience Requirements:
Master's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and three (3) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain.
Or, in the alternative:
Bachelor's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and five (5) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain.
Required Skills:
Experience must include 2 years of experience in each of the following skills:
(1) Programming applications using Python, R, SQL, and SAS to extract and transform data from multiple data sources;
(2) Performing data wrangling and matching leveraging Extract Load Transfer (ETL) techniques;
(3) Performing parallel computing, distributed computing, and distributed data processing leveraging large-scale data on Hadoop and Spark system;
(4) Performing feature engineering and selection (transformation, binning, and high-level categorical reduction); model optimization (grid search and Bayesian optimization); and, model testing and validation (cross validation and bootstrapping); and,
(5) Developing and deploying supervised and unsupervised machine-learning models leveraging Random Forest, XGBoost, and GBM tree models, deep learning, and k-means; and performing regularization leveraging Ridge, Lasso, and elastic nets.
Eligible for Employee Referral Program
Overtime eligible: Exempt
Discretionary bonus eligible: No
Sales bonus eligible: No
Click here to learn more about our benefits . Starting salary is dependent upon several factors including previous work experience, specific industry experience, and/or skills required.
Location Designation: Hybrid
Employer: New York Life Insurance Company
Job Title: Senior Associate, Data Scientist
Location: This position reports to the NY Life Headquarters in New York, NY but applicants may work from a Home Office from anywhere in the United States
Offered Wage: $178,200.10/year
Duties: As part of the company's Center for Data Science and Artificial Intelligence (CDSAi) corporate analytics group, applies analytical skills to work on all aspects of the life insurance value chain, ranging from risk models, fraud detection, customer behavior study, process triaging, and marketing prediction to a variety of other analytics solutions. Applies technical data, analytical, and programming skills to ingest, wrangle, and explore external and internal data to create data assets and reports. Functions as the data expert and prepares data for modeling, supports production deployment of models, and builds world-class machine-learning models to solve tangible business problems. Contributes to data analysis and modeling projects from project and sample design, business review meetings with internal and external clients to determine requirements and deliverables, and the receipt and processing of data. Performs analyses and modeling for final reports and presentations, communicates results, implements support, and 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. Leverages advanced statistical and machine-learning techniques to create high-performing predictive models and creative analyses 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. Implements analytical models into production by collaborating with internal Technology and Operation teams.
Education & Experience Requirements:
Master's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and three (3) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain.
Or, in the alternative:
Bachelor's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and five (5) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain.
Required Skills:
Experience must include 2 years of experience in each of the following skills:
(1) Programming applications using Python, R, SQL, and SAS to extract and transform data from multiple data sources;
(2) Performing data wrangling and matching leveraging Extract Load Transfer (ETL) techniques;
(3) Performing parallel computing, distributed computing, and distributed data processing leveraging large-scale data on Hadoop and Spark system;
(4) Performing feature engineering and selection (transformation, binning, and high-level categorical reduction); model optimization (grid search and Bayesian optimization); and, model testing and validation (cross validation and bootstrapping); and,
(5) Developing and deploying supervised and unsupervised machine-learning models leveraging Random Forest, XGBoost, and GBM tree models, deep learning, and k-means; and performing regularization leveraging Ridge, Lasso, and elastic nets.
Eligible for Employee Referral Program
Overtime eligible: Exempt
Discretionary bonus eligible: No
Sales bonus eligible: No
Click here to learn more about our benefits . Starting salary is dependent upon several factors including previous work experience, specific industry experience, and/or skills required.