Data Scientist Lead - Model Development

About the role

The Data Scientist Lead will work closely with the Data Science Director to ensure that AI/ML modeling solutions are successfully developed and implemented. This will involve supervising multiple projects/initiatives, providing technical expertise, ensuring quality results, and mentor and leading Senior and Junior Data Scientists.

Responsibilities

• Gathers, interprets, and manipulates complex structured and unstructured data to enable advanced analytical solutions for the business.
• Ledds and conducts advanced analytics leveraging 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 COMPLEX MODELING PROBLEMS/RESEARCH EFFORTS.
• MANAGES PROJECT PORTFOLIO MILESTONES, RISKS, AND IMPEDIMENTS.
• ANTICIPATES POTENTIAL ISSUES THAT COULD LIMIT PROJECT SUCCESS OR IMPLEMENTATION AND ESCALATES AS NEEDED.
• ESTABLISHES AND MAINTAINS BEST PRACTICES 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 CUTTING-EDGE 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.
• SUREKS 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 discipline; 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 OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 6 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 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, HQL, 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.
• 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, forest models, etc.
• 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.
• Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within the organization.

Qualifications

• Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; 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 OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 6 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 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, HQL, 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.
• 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, forest models, etc.
• 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.
• Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within the organization.

Skills

• Extensive model building experience (preferably in banking) covering a variety of AI/ML and traditional statistical modeling approaches.
• Extensive hands-on experience preparing data and code for modeling.
• Experience with the entire model lifecycle, including conceptualization, development, implementation, validation, and ongoing performance monitoring.
• Experience working directly with clients, customers, or internal business partners.
• Experience leading client or customer relationships and expectations, including senior leadership.

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

$164,780 - $314,960

Schedule

Remote eligible in the continental U.S. with occasional business travel. Individuals residing within a 60-mile radius of a USAA office will be expected to work on-site four days per week. Relocation assistance is available for this position.

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