Data Scientist (Mid-level) - Risk Modeling

usaa; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

USAA is seeking a Data Scientist to join the P&C Underwriting Data Science team. The role focuses on analyzing data, prototyping models, and deploying data science products in operations.

The candidate will work across data engineering and IT to deliver production-ready analytics in a hybrid work setting from one of several US locations. The position requires strong ML, statistical skills, and experience with Python, SQL, and data workflows.

Qualifications

• Bachelor's degree or four years of relevant education/experience in quantitative field.
• Four years of experience in predictive analytics or data analysis; advanced degree may substitute for experience.
• Two years of experience in training and validating statistical or ML models.
• Two years of experience in one or more dynamic scripting languages (Python, R) for statistics or ML.

Responsibilities

• Gathers, interprets, and processes structured and unstructured data for analytics.
• Develops scalable ML, simulation, and optimization solutions to deliver insights and value.
• Selects modeling techniques considering data constraints and business needs.
• Deploys models within MDC and MRM frameworks and documents processes.

Skills

Statistical analysis

Machine learning

Python

SQL

Education

Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial science, science, engineering, or quantitative field

Advanced degree (Master's/PhD) in quantitative field

Tools

Python

R

SQL

HQL

NoSQL

Job description

Why USAA?

At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.

Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful.

We are proud to support active‑duty military spouses. USAA roles may offer remote or hybrid flexibility for active‑duty military spouses consistent with applicable policy and business needs.

The Opportunity

We are looking for a highly skilled Data Scientist to join the P&C Underwriting Data Science team. This role will be tackling some of our most challenging analytical problems in P&C Underwriting by experimenting with data, prototyping statistical models, and deploying new data science products in operations. We're looking for someone with a high level of curiosity that can ask the right questions and has the technical skills to test business hypothesis quickly in an iterative way.

We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Phoenix, AZ, Plano, TX, Tampa, FL or Colorado Springs, CO.

Relocation assistance is not available for this position.

What you’ll do:

• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes technical documents for knowledge persistence, risk management, and technical review audiences.
• Assesses business needs to propose/recommend analytical and modeling projects to add business value.
• Participates in the prioritization of analytics and modeling problems/research efforts with business and analytics leaders.
• Contributes to the development of a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
• Translates business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
• Works closely with Data Engineering, IT, the business, and other internal stakeholders to deploy production-ready analytical assets that are aligned with the customer's vision and specifications while being consistent with modeling best practices and model risk management standards.
• Maintains awareness of cutting‑edge techniques.
• Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

What you have:

• Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience.
• 4 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 2 years of experience in predictive analytics or data analysis.
• 2 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 2 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
• Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
• Experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
• Experience in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Ability to assess regulatory implications and expectations of distinct modeling efforts.
• Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant