Applied Scientist: Fraud & Inventory Analytics

Amazon; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Restricted stock units (RSUs)

Health insurance

Paid time off

Job summary

Amazon is seeking an Applied Scientist on the WWOS Tech team to tackle theft, fraud, and organized crime across our global supply chain. You will own business challenges from day one, building ML-driven solutions and presenting findings to leadership.

Ideal candidates hold a PhD or Master’s with strong ML and data research experience, capable of independent decision-making in ambiguous environments and collaborating with cross-functional teams.

Qualifications

• 3+ years of building models for business application experience.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Responsibilities

• Own KPIs that measure theft/fraud management performance and efficiencies.
• Detect and automate theft, fraud MOs.
• Detect organized crime rings and bad actor clusters.
• Perform end to end evaluation of operational defects, system gaps, and scaling challenges (both system and operational).
• Contribute to the overall fraud management and product development strategies.
• Present key learnings and vision to stakeholders and leadership.
• Integrate ML detection models via software applications

Skills

Java

C++

Python

ML modeling

Education

PhD in Research/Statistics/Engineering/Supply Chain

Master's + 4+ years CS/CE/ML

Tools

Unix/Linux

Job description

Amazon is seeking an Applied Scientist on the WWOS Tech team to tackle theft, fraud, and organized crime across our global supply chain. You will own business challenges from day one, building ML-driven solutions and presenting findings to leadership.

Ideal candidates hold a PhD or Master’s with strong ML and data research experience, capable of independent decision-making in ambiguous environments and collaborating with cross-functional teams.