Risk Mining Analyst II Account Integrity

Description

Amazon's Account Integrity team (AIT) within the Customer Partner Trust organization is looking for a passionate, results-oriented Risk Mining Analyst (similar to business analyst) to leverage data, detect anomalies and prevent bad actors for harming our genuine customers. The incumbent will lead one of the core verticals of Risk Mining with AIT. The AIT team owns designing and building high performance software systems using machine learning that identify and prevent fraudulent activity and maintain high trust levels with our customers. Fraud and abuse prevention is a real-money game where our software and analytics teams strive to outsmart those who attempt to defraud Amazon and our customers.
As a Business Analyst for Risk Mining in the Account Integrity group, you will be responsible for analyzing large data sets in partnership with product managers, scientists, and engineers, drive root cause analysis of ever changing bad actor behaviours and deploy rules that will prevent bad actors from gaining access to Amazon's platform.

Note: The standard number of days for the role is 5 but will be required to work on either Tue- Sat or Sun - Fri with Sat or Sun as a fixed working day (with a week off on a standard work day).This is to provide round the clock coverage across Risk Mining.

Key job responsibilities
- Create, maintain, and improve data sets, pipelines and reporting to track and manage important KPIs and goals in the Account Integrity space
- Analyze complex customer trends to identify patterns, develop attritbutes that clearly articulate those pattern and incorporate those attributes into static rules for automated deployment in our account creation pipeline.
- Operate as an on-call during rotation weeks to respond to high severity events impacting Amazon
- Apply your expertise in quantitative analysis, data visualization and data-mining to design alarm systems, derive actionable insights and guide product strategy for stakeholders and leadership
- Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. within SQL and other BI tools
- Support cross-functional teams on the day-to-day execution of projects and initiatives
- Enable effective decision making by retrieving and aggregating data from multiple sources and compiling it into a digestible and actionable format
- Communicate complex analysis and insights to stakeholders and business leaders, both verbally and in writing
- Use AI tools / IDEs available in Amazon to develop pipelines, query information, automate workflows, develop risk detection alarms etc. using local or cloud desktops / AWS tools.

A day in the life
1. Review instances of bad actor account activity and create mechanisms to stop them
2. Report on fraud and abuse trends and improve products
3. Create and maintain dashboards that detect and inform stakeholders (such as downstream payment and abuse product owners) on how to stop fraud
4. Participate and lead process improvement initiatives across account integrity teams
5. Develop rules to mitigate bad actor activity

About the team
Our team consists of Business Analysts and Risk Mining specialists rolle up under the Account integrity Machine Learning Team. We evaluate instances of organized bad actor account take over and creation of fraudulent accounts on Amazon . We collaborate closely across multiple functions in order to mitigate these attacks through various short and long term solutions.

Basic Qualifications

- 4+ years of compliance program management, legal, governance, audit, risk/loss prevention, or equivalent experience
- Bachelor's degree or equivalent
- Experience handling confidential information
- Experience with SQL and Excel
- 2+ years of Excel (including VBA, pivot tables, array functions, power pivots, etc.) and data visualization tools such as Tableau experience

Preferred Qualifications

- 3+ years of program requirements definition and data and metrics leveraging to dri Informática | Sistemas

Requerido

Universidad Completa | Graduado