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USAA · 2 hours ago

Lead Graph Data Scientist - Identity Analytics

Colorado Springs, CO

Full-time

Onsite

Lead/Staff

$165K/yr - $297K/yr

8+ years exp

USAA is committed to empowering its members to achieve financial security through exceptional service and innovative products. The Lead Graph Data Scientist - Identity Analytics will develop and implement quantitative solutions to detect and prevent identity theft and fraud, utilizing advanced analytics and machine learning techniques.

BankingFinancial ServicesInsuranceVenture Capital

No H1B

Responsibilities

Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and negative member experience from fraud application, synthetic fraud and account takeover attempts

Closely partner with Strategies team, Director of Fraud Identity Analytics and Director of Fraud Model Management and Model Users on model builds and priorities

Partner with Technology and other key collaborators to deploy a Financial Crimes graph database strategy, including vendor selection, business requirements, data needs, and clear use cases spanning financial crimes

Deploy graph databases and graph techniques to identify criminal networks engaging in fraud, scams, disputes/claims and AML and deliver highly significant benefits

Generate and prioritize fraud-dense rings to mitigate losses and improve Member experience

Identify and work with technology to integrate new data sources for models and graphs to augment predictive power and improve business performance

Exports insights to decision systems to enable better fraud targeting and model development efforts

Drives continuous innovation in modeling efforts including advanced techniques like graph neural networks

Develops and mentors junior staff, establishing a culture of R&D to augment the day-to-day aspects of the job

Gathers, interprets, and manipulates sophisticated structured and unstructured data to enable sophisticated analytical solutions for the business

Leads and conducts sophisticated analytics demonstrating machine learning, simulation, and optimization to deliver business insights and achieve business objectives

Guides team on selecting 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 peer reviews technical documents for knowledge persistence, risk management, and technical review audiences

Partners with business leaders from across the organization to proactively identify business needs and proposes/recommends analytical and modeling projects to generate business value

Works with business and analytics leaders to prioritize analytics and highly sophisticated modeling problems/research efforts

Leads efforts to build and maintain 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

Assists team with translating business request(s) into specific analytical questions, implementing analysis and/or modeling, and communicating outcomes to non-technical business colleagues with a focus on business action and recommendations

Manages project portfolio breakthroughs, risks, and impediments. Anticipates potential issues that could limit project success or implementation and intensifies as needed

Establishes and maintains standard methodologies for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards

Interacts with internal and external peers and management to maintain expertise and awareness of pioneering techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies

Serves as a mentor to data scientists in modeling, analytics, computer science, eye for business, and other interpersonal skills

Participates in enterprise-level efforts to 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

Qualification

Machine LearningGraph AnalyticsPredictive AnalyticsStatistical ModelingPythonSQLProject ManagementCommunication SkillsMentoringTeam Collaboration

Required

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 (in addition to the minimum years of experience required) may be substituted in lieu of degree

8 years of experience in a predictive analytics or data analysis

6 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

Expert ability to write 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, 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

Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics

Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts

Project management experience that demonstrates the ability to anticipate and appropriately manage project landmarks, risks, and impediments. Demonstrated history of appropriately communicating potential issues that could limit project success or implementation

Expert level experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, and ensemble methods such as Random Forests, XGBoost, LightGBM, and CatBoost

Expert level experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc

Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building

Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science

A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent)

Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization