Senior Associate

Job Requisition ID: 91166

Location Designation: Fully Onsite 

Job Title:  Senior Associate

Location Designation: Fully In Office - 51 Madison Avenue, New York, New York 10010

Offered Wage: $170,373.00/yr

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, Machine-Learning, Mathematics or related quantitative discipline (willing to accept foreign education equivalent) plus three (3) years of experience performing data analytics and statistical modeling using large, complex datasets or, alternatively, a Bachelor's degree in Statistics, Machine-Learning, Mathematics or related quantitative discipline (willing to accept foreign education equivalent) and five (5) years of experience performing data analytics and statistical modeling using large, complex datasets.

Experience must include 2 years in all components of each of the following skills: Applying and developing novel statistical methodology to improve business outcomes and increase revenues (within the consumer finance or insurance industry) leveraging quantitative tools and statistics theories, including linear mixed effects models, generalized additive models, neural network, gradient boosting models, uplift modeling, design of experiment for model building, Bayesian analysis, robustness analysis, L1/L2 regularization, Ridge, Lasso, elastic nets regularization for model tuning and dimension reduction, bootstrapping, model diagnostics, Markov chain Monte Carlo (MCMC) for model inference, spline functions, entity exactions, encoding, tokenization, fuzzy matching, text normalization, natural language processing for data pre-processing and feature engineering, precision-recall curves, Gini, lift and gain charts, A/E for model performance assessment, L2X, and SHAP value for model explanation; Automating analysis processes and managing large-scale data leveraging Spark, Hadoop, AWS S3, EC2, and Sagemaker to ensure efficiency, scalability, and cost effectiveness; Deploying real-time models into production by writing production-ready code and modularizing complex solution design leveraging R, Python SAS, and SQL for production efficiency and scalability and designing and conducting rigorous unit stress tests leveraging pytest, conda virtual environment, and Docker container for results validation; Collaborating with cross-functional teams of Product Managers, Engineers, Model Validation teams, business leaders, and front-line field workers to design and implement business project proposals to measure the impact of business objective changes and