Senior Associate, Data Scientist

Key Skills

Aartificial intelligence, Data science techniques, Deep Learning, Design, ETL frameworks, LightGBM, Machine learning techniques, Optimization, Python Programming, R Programming, Random forests, SAS, SQL, Statistics, XGBoost

Job Description

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.