Lead Graph Data Scientist – Identity Analytics

Role Overview

• 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.
• 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.

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 (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.

Tech Stack

• NoSQL
• Python
• SQL

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