Data Scientist – Mid-Level

• Translate business problems into applied statistical, machine learning, simulation, and optimization solutions
• Deliver actionable business insights and business value through automation, revenue generation, and expense and risk reduction
• Collaborate with engineering partners to deliver scalable solutions and customer-facing applications
• Use databases, cloud technologies, and programming to build analytical modeling solutions
• Enhance USAA's tools and expand its library of internal packages and applications
• Partner with Model Risk Management to validate model results and ensure model stability before production deployment
• Gather, interpret, and manipulate structured and unstructured data
• Develop scalable, automated solutions using machine learning, simulation, and optimization
• Select modeling techniques and technologies based on data limitations, applications, and business needs
• Develop and deploy models within the Model Development Control and Model Risk Management frameworks
• Compose technical documentation for knowledge persistence, risk management, and technical review audiences
• Assess business needs and recommend analytical and modeling projects
• Participate in prioritizing analytics and modeling problems and research efforts
• Contribute to a reusable, production-quality library of algorithms and supporting code
• Translate business requests into analytical questions, execute analyses/models, and communicate outcomes to non-technical colleagues
• Work with Data Engineering, IT, business teams, and internal stakeholders to deploy production-ready analytical assets
• Maintain awareness of cutting-edge techniques and seek opportunities to learn new techniques, technologies, and methodologies
• Identify, measure, monitor, and control risks in accordance with risk and compliance policies and procedures

Requirements

• Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or a quantitative field; OR 4 years of relevant education and/or experience
• 4 years of experience in predictive analytics or data analysis OR an advanced degree and 2 years of experience in predictive analytics or data analysis
• 2 years of experience training and validating statistical, physical, machine learning, and other advanced analytics models
• 2 years of experience with a dynamic scripted language such as Python or R for statistical analyses and/or building and scoring AI/ML models
• Experience writing clear, well-documented, and transparent code
• Experience querying and preprocessing structured and/or unstructured database data using SQL, HQL, NoSQL, or similar query languages
• Experience working with structured, semi-structured, and unstructured data files, including delimited numeric files, JSON/XML files, text documents, and images
• Experience performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics
• Ability to assess regulatory implications and expectations of distinct modeling efforts
• Experience with supervised modeling techniques including linear/logistic regression, discriminant analysis, support vector machines, decision trees, and forest models
• Experience with unsupervised modeling techniques including k-means clustering, hierarchical/agglomerative clustering, neighbor algorithms, and DBSCAN
• Experience communicating analytical and modeling results to non-technical business partners with actionable recommendations
• Must not require immigration support or visa sponsorship now or in the future

Core Competencies

Demonstrates expertise in predictive analytics, machine learning, and statistical modeling, with a strong ability to translate complex data into actionable business insights. Proficient in collaborating with cross-functional teams to develop scalable analytical solutions and communicate results effectively to non-technical stakeholders.

Highest-signal resume keywords

• Predictive Analytics
• Machine Learning
• Statistical Modeling
• Python Programming
• SQL Querying

Hard Skills

• Predictive Analytics
• Statistical Modeling
• Machine Learning
• Data Analysis
• SQL
• Python
• R
• Linear Regression
• K-Means Clustering
• Decision Trees

Soft Skills

• Communication
• Collaboration
• Problem-Solving
• Documentation

Industry Keywords

• Model Risk Management
• Risk Compliance
• Data Manipulation
• Analytical Solutions
• Business Insights

Tools & Technologies

• Cloud Technologies
• Databases
• AI/ML Models
• Data Engineering