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Risk Analyst, Buyer Risk Prevention, Payment Risk Mining

Amazon Development Centre (India) Private Limited - S55 -
Hyderabad, Telangana

Apply Now

Job details

Full-time

Qualifications

Doctoral degree
Computer Science
Relational databases
R
Financial analysis
Databases
SQL
Bachelor's degree
Machine learning
Scripting
ETL
Fraud prevention and detection
Python

Full job description

DESCRIPTION

Have you ever thought about what it takes to detect and prevent fraudulent activity in hundreds of millions of eCommerce transactions across the globe? What would you do to increase trust in an online marketplace where millions of buyers and sellers transact? How would you build systems that evolve over time to proactively identify and neutralize new and emerging fraud threats?

Our mission in Buyer Risk Prevention (BRP) is to make Amazon.com the safest place to transact online. BRP safeguards every financial transaction across all Amazon sites, while striving to ensure that these efforts are transparent to our legitimate customers. As such, BRP designs and builds the software systems, risk models and operational processes that minimize risk and maximize trust in Amazon.com.

As a Risk Analyst in BRP, you will be responsible for analyzing terabytes of data to identify specific instances of risk, broader risk trends and points of customer friction. You will develop scalable solutions for fraud prevention. In addition, you will be responsible for building a robust set of operational and business metrics and will utilize metrics to determine improvement opportunities.

Key job responsibilities
Perform complex data analysis to proactively generate actionable insights

Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. within SQL and other BI tools

Design and implement various visual dashboards in order to make insights available to team members, stakeholders and our customers

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

Preparing and delivering business reviews to the senior management team regarding progress and roadblocks

Communicate complex analysis and insights to stakeholders and business leaders, both verbally and in writing

A day in the life
1. Review instances of fraud and create mechanisms to stop them
2. Report on fraud trends and improve products
3. Create and maintain dashboards that detect and inform stakeholders (such as payment product owners) on how to stop fraud

About the team
Our team consists of Business Analysts, Data Scientists, Business Intelligence Engineers and Applied Scientist rolling under the core Buyer Risk Prevention Machine Learning Team. We evaluate instances of organized buyer fraud attacks on Amazon platform WW across various payment methods. We collaborate closely across multiple functions in order to mitigate these attacks through various short and long term solutions.

BASIC QUALIFICATIONS

1+ years of business or financial analysis experience
Experience with SQL or ETL
Experience defining requirements and using data and metrics to draw business insights

PREFERRED QUALIFICATIONS

Bachelor's degree or above in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or PhD
1+ years of professional work experience, or experience in SQL or other relational databases
Knowledge of a scripting language (Python, R, etc.)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Apply Now

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