Data Scientist II – Fraud
USAA
full-time
Posted on:
Location Type: Hybrid
Location: San Antonio • Arizona, Colorado, Florida, North Carolina, Texas • 🇺🇸 United States
Visit company websiteSalary
💰 $93,770 - $168,790 per year
Job Level
JuniorMid-Level
Tech Stack
NoSQLPythonSQL
About the role
- Responsible for the development of machine learning models that improve fraud detection and prevention
- Develop and continuously update internal fraud models
- Work with Strategies and Model Management teams to understand and plan model needs
- Drive continuous innovation in modeling efforts
- Collaborate with the broader analytics community
- Capture, interpret, and manipulate structured/unstructured data for analytical solutions
- Select appropriate modeling technique and/or technology
- Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework
- Compose technical documents for knowledge persistence and risk management
- Consult with Data Engineering, IT, and other internal partners to deploy solutions aligned with the customer’s vision
Requirements
- Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience may be substituted in lieu of degree
- 2 years of experience in predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in relevant field
- Experience in training and validating statistical, physical, machine learning, and other sophisticated analytics models
- Experience in one or more multifaceted scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models
- Ability to write code that is easy to follow, well detailed, and commented where vital to explain logic
- 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
- Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics
- Experience with classical supervised modeling for prediction such as linear/logistic regression, decision trees, etc.
- Experience with concepts associated with unsupervised modeling such as k-means clustering, neighbors algorithms, etc.
- Ability to communicate analytical and modeling results to non-technical business partners
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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningpredictive analyticsdata analysisstatistical modelingPythonRSQLHQLNoSQLlinear regression
Soft skills
communicationcollaborationinnovationproblem-solvinganalytical thinking