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Software Engineer -CFRT Decision Engine 17242 8/9/2023 2:52:00 PM

IT
Contractor - W2

Job Description

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.
 

Job Requirements

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.