U.S. Bank Jobs

Job Information

U.S. Bank Distinguished AI Engineer - Fraud in Charlotte, North Carolina

At U.S. Bank, we're passionate about helping customers and the communities where we live and work. The fifth-largest bank in the United States, we’re one of the country's most respected, innovative and successful financial institutions. U.S. Bank is an equal opportunity employer committed to creating a diverse workforce. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, among other factors.

Job DescriptionWe are seeking an exceptional hands-on technical leader with a proven track record to join the U.S. Bank Enterprise Analytics and Artificial Intelligence (EAA) group. This team is responsible for advancing the applied use of Artificial Intelligence and Machine Learning at U.S. Bank and showcasing the potential for AI through early-stage solutions (e.g. “art of the possible”).

Distinguished Engineers are recognized as experts in one or more domains within U.S. Bank and across the industry. They represent the senior technical experts within the organization and have a strong track record of growing and influencing others.

As a Distinguished Engineer you will be working with senior leaders, business translators, business units, and partners to influence business and technology roadmaps and the adoption. In addition, you will mentor and grow other domain experts within the organization. This is an executive-level individual contributor role representing U.S. Bank’s AI/ML capabilities in internal and external forums and requiring technical acumen as well as the ability to simplify concepts for cross functional teams.

The ideal candidate will have previously served in a leadership capacity in the use of advanced AI/ML algorithms to support fraud detection in the financial industry.

Take on some of technology’s greatest challenges and make a significant impact to millions of users by leveraging the bank’s unique data assets to drive groundbreaking work in AI/ML that can be applied at massive scale across U.S. Bank lines of business.

Required Qualifications

  • Graduate degree (PhD strongly preferred) in a quantitative discipline, e.g. Engineering, Computer Science, Mathematics, Operations Research, Data Science, and 8+ years of experience in a highly quantitative position.
  • 5 years of experience related to the fraud business domain within a financial institution
  • 5 years of experience deploying models & strategies to fraud management systems within a financial institution
  • 5 years of experience in AI/ML applied to develop custom fraud models within a financial institution: 1st and 3rd party fraud

Origination and transaction fraud

Product domains such as Credit Card, Loans, Deposits, etc.

  • Ability to extrapolate emerging AI techniques and their applications to construct roadmaps for future applications. Ability to understand, explore, slice and dice structured and unstructured data sources and quickly identify the AI/ML techniques appropriate for analysis.
  • Comprehensive knowledge of modern and cutting-edge AI and ML techniques, tools, and best practices
  • Ability to develop and debug in at least one of Python, Java, C, MATLAB. Proficient in git version control.
  • Experience in computer vision techniques that involve object classification, object detection and other techniques with different backbones such as Resnet, or mobile Nets OR
  • Experience in natural language processing techniques that use sequence to sequence, Transformers ( e.g. BERT, RoBERTa, GPT2 etc.) applied to NLP techniques such as entity recognition, classification, question answering, summarization: Ability to design, tune and evaluate key metrics of your model’s performance which are aligned with business goals.

Proven track record in creating and managing proof-of-concept (POC) projects and successful knowledge transfer of POC concepts to production level implementations.

Proven track record of strong verbal/written communication and presentation skills, including an ability to effectively communicate with both business and technical teams.

Preferred Qualifications

  • Experience with time series data, sequential data or graph data analysis for anomaly detection, classification and forecasting.
  • Strong background in Mathematics and Statistics
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal or open-source contribution
  • Extensive experience with AI and ML software and computational packages (e.g., PyTorch, TensorFlow, mxnet) and python packages (scikit-learn, numpy, pandas, OpenCV).
  • Experience with reinforcement learning, convex optimization and graph neural networks is a plus.
  • High enthusiasm, integrity, ingenuity, results-orientation, self-motivation, and resourcefulness in a fast-paced environment

Benefits: Take care of yourself and your family with U.S. Bank employee benefits. We know that healthy employees are happy employees, and we believe that work/life balance should be easy to achieve. That's why we share the cost of benefits and offer a variety of programs, resources and support you need to bring your full self to work and stay present and committed to the people who matter most - your family.

Learn all about U.S. Bank employee benefits, including tuition reimbursement, retirement plans and more, by visiting usbank.com/careers.

EEO is the Law Applicants can learn more about the company’s status as an equal opportunity employer by viewing the federal EEO is the Law poster.

E-Verify U.S. Bank participates in the U.S. Department of Homeland Security E-Verify program in all facilities located in the United States and certain U.S. territories. The E-Verify program is an Internet-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services. Learn more about the E-Verify program.

U.S. Bank is an equal opportunity employer committed to creating a diverse workforce. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, among other factors.