Credit & Fraud Risk Technology
Internal notes: CFRT Decision Engine DEV
Summary
The Software engineer, Credit and Fraud risk technology is responsible for building and maintaining risk decision systems that scale across products with a unified approach to rule/business logic management. Part of the Credit & Fraud risk technology group, this individual will contribute to the design and delivery of robust, scalable microservices in support of digital bank's mission-critical decisioning infrastructure.
Position description:
· Build, unit test and implement- risk/business strategies, models, scorecards on DBU's decisioning platform
· Build, test and maintain DE APIs to access and consolidate internal & external data sources required for decisioning
· Collaborate with business, technology, decision sciences and product to understand proposed rule and policy changes, clarify and implement, troubleshoot, and perform hot fixes
· Triage and resolve issues reported by Strategy assurance, Risk, Operations, and business stakeholders
· Ensure efficient delivery of projects with utmost quality and meet/beat timelines
· Continue to evolve our 'Best Practice' methods to amplify delivery throughput
· Define and drive adoption of established 'Change Management' process
· Designing, automating and executing unit and integration tests
Position requirements:
· BS degree (Master's preferred) with 5+ years working in coding, testing, and monitoring decision/business rules in any decision infrastructure-based applications (Provenir, Drools, IBM ODM, GDS Link, FICO, Zoot, etc.,)
· Strong database skills
· Expert knowledge of US Credit bureau data and risk scorecards, model infrastructure
· Strong knowledge of data structures, algorithms, and design patterns
· Experience in Risk domain (Credit, Fraud, compliance)
· Preferred - Hands on experience with Java & REST API, JavaScript, python, or an equivalent programming language
· Knowledge of agile operating principles - experience operating in a sprint cycle
· Knowledge of observability platforms such as Splunk, Datadog etc.
Credit & Fraud Risk Technology
Internal notes: CFRT Decision Engine DEV
Summary
The Software engineer, Credit and Fraud risk technology is responsible for building and maintaining risk decision systems that scale across products with a unified approach to rule/business logic management. Part of the Credit & Fraud risk technology group, this individual will contribute to the design and delivery of robust, scalable microservices in support of digital bank's mission-critical decisioning infrastructure.
Position description:
· Build, unit test and implement- risk/business strategies, models, scorecards on DBU's decisioning platform
· Build, test and maintain DE APIs to access and consolidate internal & external data sources required for decisioning
· Collaborate with business, technology, decision sciences and product to understand proposed rule and policy changes, clarify and implement, troubleshoot, and perform hot fixes
· Triage and resolve issues reported by Strategy assurance, Risk, Operations, and business stakeholders
· Ensure efficient delivery of projects with utmost quality and meet/beat timelines
· Continue to evolve our 'Best Practice' methods to amplify delivery throughput
· Define and drive adoption of established 'Change Management' process
· Designing, automating and executing unit and integration tests
Position requirements:
· BS degree (Master's preferred) with 5+ years working in coding, testing, and monitoring decision/business rules in any decision infrastructure-based applications (Provenir, Drools, IBM ODM, GDS Link, FICO, Zoot, etc.,)
· Strong database skills
· Expert knowledge of US Credit bureau data and risk scorecards, model infrastructure
· Strong knowledge of data structures, algorithms, and design patterns
· Experience in Risk domain (Credit, Fraud, compliance)
· Preferred - Hands on experience with Java & REST API, JavaScript, python, or an equivalent programming language
· Knowledge of agile operating principles - experience operating in a sprint cycle
· Knowledge of observability platforms such as Splunk, Datadog etc.