Lead Graph Data Scientist - Identity Analytics
USAA · Plano, TX
- On site
- Full-time
- $240,000 / year
- Plano, TX
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Job highlights
- Develop models to detect and prevent fraud.
- Deploy graph analytics for financial crimes.
- Integrate new data sources for predictive power.
- Mentor junior data scientists and lead projects.
- Collaborate with technology and business leaders.
About the role
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 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, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, Chesapeake, VA or Tampa, FL. Relocation assistance is not available for this position.
Job Description
The Lead Graph Data Scientist - Identity Analytics is responsible for development and implementing quantitative solutions that improve USAA's ability to detect and prevent identity theft, account takeover, and first-party/synthetic fraud. These solutions range from machine learning model development to enterprise deployment of graph analytics capabilities that protect USAA and our Members from these threats. Strong candidates will be able to deliver the following work products and processes:
- Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and reduce negative member experience from fraud applications, synthetic fraud, and account takeover attempts
- Closely partner with the Strategy team, Director of Fraud Identity Analytics, Director of Fraud Model Management, and model users on model builds and priorities.
- Partner with Technology and other key collaborators to deploy a Member Protection graph technology 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, improving fraud detection and loss mitigation
- 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
What You'll Do
- 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 the team selecting the appropriate modeling technique and/or technology with consideration for 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 propose/recommend analytical and modeling projects to generate business value.
- Works with business and analytics leaders to prioritize analytics and highly sophisticated modeling problems/research initiatives.
- 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 highest-quality data.
- Assists the 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 milestones, risks, and impediments. Anticipates potential issues that could limit project success or implementation and escalates 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 leading 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, business acumen, 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.
What You Have
- 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 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 languages (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 milestones, 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, nearest-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.
What Sets You Apart
- US military experience through military service or a military spouse/domestic partner
- Graduate degree in a quantitative subject area
- Over 5 years of experience with model development or other advanced fraud detection algorithms
- Over 4 years of experience with graph databases and graph solutions
- Experience in fraud/financial crimes model development
Key skills/competency
- Graph Data Science
- Identity Analytics
- Fraud Detection
- Machine Learning
- Graph Databases
- Data Mining
- Python
- SQL
- Statistical Modeling
- Risk Management
Skills & topics
- Data Scientist
- Graph Analytics
- Machine Learning
- Fraud Detection
- Identity Analytics
- Python
- SQL
- Predictive Modeling
- Financial Crimes
- Lead Data Scientist
How to get hired
- Tailor your resume: Highlight experience in predictive analytics, machine learning, and graph databases, using keywords from the job description.
- Showcase your impact: Quantify achievements in fraud detection and loss mitigation with specific metrics and examples.
- Demonstrate leadership: Emphasize experience mentoring junior staff and managing complex projects.
- Prepare for technical interviews: Be ready to discuss your experience with Python, SQL, machine learning algorithms, and graph techniques.
- Understand USAA's mission: Research USAA's values and how your skills can contribute to member financial security.
Technical preparation
Master Python for ML and graph libraries.,Practice advanced SQL for complex data queries.,Build models using GNNs and ensemble methods.,Prepare graph database querying examples.
Behavioral questions
Describe a complex fraud detection challenge.,How do you mentor junior data scientists?,How do you explain technical concepts to non-technical audiences?,How do you manage project risks and timelines?
Frequently asked questions
- What are the key responsibilities for a Lead Graph Data Scientist at USAA?
- As a Lead Graph Data Scientist - Identity Analytics at USAA, you will be responsible for developing and implementing quantitative solutions to combat identity theft, account takeover, and various types of fraud. This includes building and updating machine learning models, deploying graph analytics, identifying criminal networks, integrating new data sources, and mentoring junior staff. Your work will directly contribute to protecting USAA and its members from financial threats.
- What technical skills are most important for this Lead Graph Data Scientist role at USAA?
- The role requires expert-level experience in predictive analytics, machine learning (supervised and unsupervised), and data analysis. Proficiency in scripting languages like Python or R, strong SQL skills for data querying, and experience with structured and unstructured data are essential. Expertise in classical modeling techniques, ensemble methods (Random Forests, XGBoost), and unsupervised methods (clustering) is crucial. Experience with graph databases and graph solutions is also a significant advantage.
- Does USAA offer remote work options for the Lead Graph Data Scientist position?
- This Lead Graph Data Scientist position offers a flexible work environment requiring 4 days in the office per week. While USAA supports active-duty military spouses with potential remote or hybrid flexibility, this specific role is based in one of the listed office locations and is not fully remote.
- What kind of experience is required for the Lead Graph Data Scientist job at USAA?
- The role requires a Bachelor's degree in a quantitative field or equivalent experience, with a minimum of 8 years in predictive analytics/data analysis and 6 years in training/validating advanced models. Additionally, 4 years of experience with scripting languages like Python/R and strong experience in data querying (SQL, NoSQL) and data file handling are necessary. Project management and mentoring experience are also key requirements.
- How does USAA approach compensation for Lead Graph Data Scientist roles?
- USAA offers a competitive salary range for this position, which is $164,780 - $314,960 annually. Actual salaries are determined based on individual experience, market data, and the specific location. Employees may also be eligible for performance-based pay incentives.
- What is the career growth potential for a Lead Graph Data Scientist at USAA?