AML Consultant

AML Consultant 10 Months Contract to Hire role Pay Range: $65 to 75/hr. on W2 ONLYPosition Location: Pittsburgh, PA, 15222 / Strongsville, OH, 44136 / Dallas, TX, 75234 / Denver, CO, 80215 Function of the Group/Initiatives: Tech team that supports the bank regulatory functions around financial crime. Primarily anti-money laundering, transaction screening, all suspicious activity screening and sanction screening. All of those kind of typical AML and sanctions functions and the related data pipelines. Industry background: Commercial/ Retail Payments industry experience required. Hiring manager has provided a rank order of priority as per below: 1. Technical subject matter expertise in areas such as AML, CDD/ KYC, Sanctions Screening and other FinCrime functions, 2. Proven practitioner of Agile/ SAFe methodology, leveraging Jira suite of tools, 3. Recent experience on projects working on migrations/ Upgrades to Nice Actimize installations, 4.SQL Very hands on with Advanced SQL/ Scripting, 5. Basic experience with Oracle/ ElasticSearch/ Mongo DB. Roles/Responsibilities: Looking to replace a very strong AML Modeling Analyst/ Software engineer + Scrum Master. Looking for someone with strong functional knowledge in the BSA space AML Sanctions, fraud, other fin crime related aspects. From a technical perspective, this is heavy on data analysis. Must have the ability to do advanced SQL query against different databases. The data shows up in different source systems. We will have data in Hadoop, Oracle, Mongo DB, Elastic Search. Create in a views report that feeds from all these sources listed above. All of our codes are predominately Java, Angular in JavaScript so we have front ends and react in Angular data back end. Data and model pipelines are written in in PySpark The lead will have to be able to deal with changes or requirements against all these aspects. Looing at behavioral models and screening models and filters. Case management- Once a suspicious transaction is found, it needs to flow through case management where you have operations and then people routing it for different kinds of investigations. Managing Agile Work; may sometimes wear the scrum master hat to manager crews as a lead. Required Skillsets: Subject matter expertise in areas such as AML, Sanctions Screening, Fraud and other FinCrime domains. Key tech knowledge: REQUIRED: Hadoop/ oracle/ ElasticSearch/ MongoDB, 8 - 15 years Tech languages: Java/ SQL/ Python/ PySpark, 8 - 15 years Financial Applications: Actimize/ Oracle FCCM/ FircoSoft/ LexisNexis suite of tools, 8 - 15 years Extensive experience on Agile/ SaFE teams leveraging Jira suite of tools, 8 - 15 years Very hands on with Advanced SQL/ python scripting, 8 - 15 years Must Have Skills (8+ Years Experience Required): 1. Actimize 2. ElasticSearch 3. Extensive experience on Agile/ SaFE teams leveraging Jira suite of tools 4. Hadoop 5. Java 6. Mongo DB 7. Oracle 8. Python/ PySpark - Very hands on with Advanced SQL / python scripting 9. SQL - Very hands on with Advanced SQL / python scripting 10. Subject matter expertise in areas such as AML, Sanctions Screening, Fraud and other FinCrime domains. Nice To Have: 1. FircoSoft (priority) 2. LexisNexis (priority) 3. Oracle FCCM (2nd priority) Pre-Screening Questions: 1. Explain if you have any experience with design or improved an AML/Fraud detection pipeline for a bank with millions of daily transactions. How did you prioritize your components. If you have not, please explain what components would you prioritize, and why? 2. Describe a time when you have experienced an AML or fraud model or ruleset that produced too many false positives or missed suspicious activity. How did you diagnose the issue, and what specific steps did you take to fix it? 3. In AML/Fraud systems, data is often incomplete, delayed, or inconsistent across source systems. How do you approach building resilient detection logic when the data is not perfect? Interview Process: 1st round Technical round; panel 1 hour, 2 rounds Situational / Behavioral 1-2 people 30 1 hour Shortlist Stack Ranking (Most Important to Least Important): 1. Skills Must have functional knowledge and tech skills, 2. Location, 3. Rate, 4. Former Client an/or Banking