This position can work remotely in the continental U.S. with occasional business travel.
We are looking for an engineering-minded and detailed-orientated Data Scientist Senior that will get to get work with the modeling team in USAA’s Auto Pricing organization. This individual will work on a new initiative called “rapid model refit” (RMR).
Translates business problems into applied statistical, machine learning, simulation, and optimization solutions to inform actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction. In collaboration with engineering partners, delivers solutions at scale, and enables customer-facing applications. Leverages database, cloud, and programming knowledge to build analytical modeling solutions using statistical and machine learning techniques. Collaborates with other data scientists to improve USAA’s tooling, growing the company’s library of internal packages and applications. Works with model risk management to validate the results and stability of models before being pushed to production at scale.
Captures, 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, and assists peers with composing, 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. Works with business and analytics leaders to prioritize analytics and modeling problems/research efforts.
Builds and maintains 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 complex 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.
Manages project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation.
Develops best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
Maintains expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
Serves as a mentor to junior data scientists in modeling, analytics, and computer science tasks.
Participates in internal communities that drive the maintenance and transformation of data science technologies and culture.
Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.
Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
6 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 4 years of experience in predictive analytics or data analysis.
4 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
4 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.
Proven experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Strong 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.
Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
Experience guiding and mentoring junior technical staff in business interactions and model building.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.
Current or previous professional experience for at least 5 years in building loss and demand models in the Property and Casualty insurance industry.
High-level of proficiency in Python programming, with hands-on experience in using various Python packages, including but not limited to pyspark, sklearn, statsmodels, xgboost, scipy, numpy, and optuna.
Highly efficient in working on large-sized datasets with heterogeneous data types by using SQL and Python. The assessment for coding might be required during the interview.
Experience using the Earnix Analytics platform.
Experience with Cloud-based analytics platform such as Sage-Maker with AWS.
Big-data solutions such as Snowflake and Apache Hive.
Current or prior experience in MLOps (Machine Learning Operations).
The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.
Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. The salary range for this position is: $138,230 - $248,810.
Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.
Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.
For more details on our outstanding benefits, please visit our benefits page on USAAjobs.com.
Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.
USAA is an equal opportunity and affirmative action employer and gives consideration for employment to qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, national origin, age, disability, genetic information, protected veteran status, or any other legally protected characteristic.