Case study on scaling hybrid moderation pipelines, blending rules, MLLLM classifiers for gray-area chats, and routing to reviewer queues. Andrei will provide end-to-end workflows with policy-aligned labeling, imbalance-aware evaluation beyond accuracy, precise thresholding, and moderator feedback loops to detect drift and adapt to shifting abuse tactics in production.
Andrei Shcherbinin is a Machine Learning Team Lead at Social Discovery Group with a background in building and scaling production ML systems for real-world products. He leads cross-functional teams delivering machine learning solutions across attribution, recommendations, automation, and monitoring, with a focus on reliable ML infrastructure and measurable business impact.
Previously a Senior ML Engineer at the same company, he developed ML monitoring platforms, marketing attribution models, and high-precision NLP systems including automated support chatbots. Earlier roles include Data Scientist positions at Simple App and FunBox, where he worked on LLM applications, personalization systems, telecom analytics, and large-scale data pipelines.
His work spans technologies such as Python, Kubernetes, AWS, Airflow, Spark, and modern LLM tooling, with an emphasis on production ML, automation, and scalable data workflows.
