Hybrid Mid-Level Data Scientist

at USAA

• Job title: Mid-Level Data Scientist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $114,080 - $218,030 per year
• Degree requirement: Bachelor's Degree
• Tech skills: NoSQL, Python, SQL
• Location requirements: Hybrid, San Antonio, United States

About the role

• Mid-Level Data Scientist leveraging data science techniques for USAA's financial security solutions. Collaborating with various teams to develop advanced analytics and model deployment practices.

Responsibilities

• 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.

Requirements

• 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 may be substituted in lieu of degree.
• 4 years of experience in 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 analysis, support vector machines, decision trees, forest models, etc.
• Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
• Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

Benefits

• 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
• various wellness programs
• career path planning and continuing education