Senior Staff Applied Scientist
Core
Design and implement end-to-end ML systems for commerce risk detection, specifically building an intelligent counterfeit detection system at scale to protect customer trust and market competition.
Role type
Senior Staff Applied Scientist (ML Systems & Risk Detection)
Builds
Intelligent counterfeit detection systems and ML pipelines for global commerce operations
Domain
E-commerce, Supply Chain, Fraud Detection
Deliverable
production ML models
Required skills
Machine learning, deep learning, statistical modeling, Python, Java, production-grade ML systems, classic ML algorithms, transformer-based models, neural networks
Preferred skills
TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, H2o.ai, Airflow, MLflow, Kubeflow, Amazon SageMaker, fraud detection domain knowledge, logistics domain knowledge
Technologies
Apache Spark, Airflow, Kubeflow, MLflow, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, H2o.ai, Amazon SageMaker
Responsibilities
Design and implement end-to-end ML systems for commerce risk detection, formulate real-world business problems as ML problems, build and optimize ML pipelines, define and track key performance metrics, collaborate with product and engineering teams, mentor engineering talent
Seniority
Senior Staff, hands-on IC with mentorship responsibilities
