Amazon Pay

FinTech

BusinessAnalyst

₹9–13L ~AI est. Bengaluru, Karnataka, India FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Business Analyst at Amazon Pay. Skills: Fraud prevention, Abuse prevention, Data analysis, Automation. Automate dashboards. Monitor key success metrics”

What You'll Achieve.

Optimize on the platform; Reduce process gaps; Prevent fraud and abuse

Industry & Context.

FinTech
Problems you'll solve

Identifying problem areas; Identifying business challenges; Root cause deep dives; Deep diving through data analysis

What They're Looking For.

Must Have

1+ years of data analytics experience, 1+ years of automation experience, 1+ years of capacity planning experience, 1+ years of operations planning experience, 1+ years of business analysis experience, Bachelor's degree, Knowledge of data pipelining, Knowledge of data extraction using SQL, Knowledge of SQL, Knowledge of Excel

Nice to Have

Knowledge of SQL/Python/R, Scripting knowledge, MS Excel knowledge, Table joins knowledge, Aggregate analytical functions knowledge, Data Analysis using GenAI tools, Deep Dive using GenAI tools

What You'll Do.

Monitor key success metrics

Identify problem areas

Identify business challenges

Assess business performance

Keep fraud and abuse in check

Analyze new fraud and abuse patterns

Create key performance indicators

Translate data into actionable insights

Automate reporting for weekly business metrics

Identify areas of opportunity to automate

Scale ad-hoc analyses

Perform complex data analysis

Perform root cause deep dives

Identify opportunities to reduce process gaps

Build infrastructure for internal datasets

Implement maintenance strategy for internal datasets

Support swift analysis

Surface insights to prevent fraud and abuse

Surface insights to Product teams

Surface insights to ML teams

Build scalable solutions

How You'll Work.

Team & Collaboration

Partnering with stakeholders; Collaboratively shaping solutions; Work with Product teams; Work with ML teams

Communication Scope

Presenting findings

Full Job Description

Our vision is to make Amazon Pay as the most trusted, convenient and rewarding Payment Experience. As Amazon Pay expands and builds new capabilities, building customer trust will be the key to drive customer adoption and enable robust business growth. This role would be a part of the "Customer Trust" team in Amazon Pay. We are responsible for driving fraud and abuse prevention initiatives for Amazon Pay products and use cases. Working in a dynamic environment, you will be responsible for automating dashboards, monitoring key success metrics, managing WBR reports, identifying problem areas and business challenges and collaboratively shaping solutions with Amazon Pay teams to help optimize on the platform. The successful candidate has a passion for extracting actionable insights from data. The candidate needs to innovate, and quickly become a subject matter expert to assess business performance across different Amazon Pay areas and help in keeping fraud and abuse in check. The candidate should have significant experience working with automation, analyzing data, identifying trends, extracting conclusions, and presenting findings in a simple and clear manner using data across various data marts, and align focus on Amazon’s strategic needs. Key job responsibilities Key Responsibilities: · Understanding drivers, impacts, and key influences in the Fraud and Abuse Domain. · Ability to analyses new fraud and abuse patterns and Partnering with the stakeholders to create key performance indicators · Translating data into actionable insights for the stakeholders. · Automate reporting for weekly business metrics, identify areas of opportunity to automate and scale ad-hoc analyses · You will perform complex data analysis (root cause deep dives, correlations, regressions, simulations, and optimization) to identify different opportunities to reduce process gaps. ·You will build infrastructure and implement maintenance strategy for internal datasets to support swift analysis to answ

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