Speakers

Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. To reduce false positives, identifying what is normal is critical. Once detected, analyzing the impact of the anomaly and prioritizing response is also critical for the system to succeed. In this session, we present a case study in payment processing at Netflix, where we use data science algorithms, scalable cloud infrastructure, and concise visualization to detect and take action on anomalies

Shankar Vederaman leads the Payment Analytics Data science and Engineering team at Netflix. His team is responsible for providing analytical solutions for Payments, Fraud and Retail gift analytics. The solutions include data engineering, BI engineering and analytical story telling. Shankar is highly passionate about creating data products that utilize the power of data science for better business benefits.