Lead Graph Data Scientist - Identity Analytics

About the role

The Lead Graph Data Scientist - Identity Analytics position at USAA is responsible for developing and implementing quantitative solutions to improve the company's ability to detect and prevent identity theft, account takeover, and first-party/synthetic fraud. This role requires a combination of advanced analytics, model development, and collaboration with cross-functional teams.

Responsibilities

• 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.

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).
• 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.
• Prominent ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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.
• Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

Qualifications

• 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).
• 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.
• Prominent ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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.
• Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

Skills

• 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.
• 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.
• Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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.
• Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

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.
• Variety of wellness programs.
• Career path planning and continuing education.

Pay

The salary range for this position is: $164,780 - $314,960.

Schedule

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.

Qualifications

• 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).
• 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.
• Prominent ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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.
• Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

Skills

• 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.
• 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.
• Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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.
• Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

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.
• Variety of wellness programs.
• Career path planning and continuing education.

Pay

The salary range for this position is: $164,780 - $314,960.

Schedule

This position can be