About the role

Data Scientist developing analytical solutions for USAA's military community. Collaborating on complex data analysis and machine learning model development.

Responsibilities

Partner with other analysts across the organization to fully define business problems and research questions.
Support SME's on cross functional matrixed teams to solve highly complex work critical to the organization.
Integrate and extract relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.
Conduct advanced analytics leveraging predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.
Support Subject Matter Experts (SME's) on efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.
Work with IT to research architecture for new products, services, and features.
Develop algorithms and supporting code such that research efforts are based on the highest quality data.
Translate complex analytical and technical concepts to non-technical employees to enable understanding and drive informed business decisions.

Bachelor’s degree in Business Analytics, Computer Science, Applied Mathematics, Quantitative Economics, Statistics or related field and 6 years of progressive, post-baccalaureate experience in job offered or in a related occupation
alternatively, a Master's degree in Business Analytics, Computer Science, Applied Mathematics, Quantitative Economics, Statistics or related field and 4 years of experience in job offered or in a related occupation
developing models using Machine Learning techniques for Prediction, Classification and Segmentation
Regression with regularization; Tree based Ensemble (Bagging- Random Forest, Boosting- CatBoost, AdaBoost, and XGBoost);
Gaussian Mixture models;
K-means/ KNN;
Neural Network and Natural Language Processing (NLP) based models;
End-to-end model development process
Coding languages & analytical tools for ML model development such as Python, PySpark and AWS Sagemaker, Google Cloud Platform, Microsoft Azure big data environments, and Git
Hadoop architecture; Snowflake, Hive, Oracle, IBM Netezza, and MySQL
Tableau and Power BI
grasp of mathematics in calculus, linear algebra, probability, and statistics
coding SQL queries
translating product, business requirements into approach statements, project plans and communicating results of advanced analytic solutions
deploying Machine Learning, statistical models into production environments and monitoring the models in production