Applied Scientist, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention

What you'll do

• Build predictive risk models, including graph-based and network analysis methods, and determine actions for different risk levels.
• Develop interpretable explanations for model decisions and translate investigation patterns into automated detection strategies.
• Partner with engineering, operations, and verification teams to deploy models and measure detection effectiveness.

What they ask for

• PhD, or a Master's degree and 4+ years of CS, CE, ML, or related field experience.
• Programming experience in Java, C++, Python, or a related language; and experience in at least one listed area such as algorithms, data mining, or distributed computing.
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and model execution optimization, or development experience in the last 3 years.
• Knowledge of deep learning, machine learning, and statistics; track record translating ambiguous business problems into well-defined ML solutions.

Nice to have

• Professional software development experience; PhD.
• Experience with adversarial ML, fraud or abuse prevention, graph-based learning, temporal or sequential data, or multi-modal data modeling.

Pay

142,800.00 - 193,200.00 USD annually (base salary)

Tools

Java, C++, Python, Machine Learning, Large Language Model

AI focus

Build AI and machine-learning systems to detect seller risk and marketplace abuse, including compromised accounts, fraudulent ownership transfers, identity manipulation, and coordinated bad-actor networks.