Build algorithms and innovative methods to discover and defend real world sophisticated cloud-based attacks in SaaS ecosystem. Collaborate with other data scientists to develop machine learning systems for detecting anomalies, compromises, fraud, and non-human identity cyber-attacks using both Gen AI and graph-based systems. Identify, integrate multiple data sources, or types of data, and develop expertise with multiple data sources to tell a story, identify new patterns and business opportunities, and communicate visually and verbally with clear and compelling data-driven stories. Analyze extensive datasets and develop a robust, scalable feature engineering pipeline within a PySpark-based environment. Work across Threat Researchers, engineering, and product teams to enable metrics for product success. Contribute to active engagement with the security ecosystem through Research papers, presentations, and blogs. Provide subject matter expertise to customers based on industry attack trends and product capabilities. 5+ years of programming language experience like C/C++/C/Python required and hands on experience in using technologies such as Spark, Azure ML, SQL, KQL, Databricks, etc. Able to prepare data pipelines and feature engineering pipelines to build robust models using SQL, PySpark, Azure Data Studio etc., Knowledge of Classification, Prediction, Anomaly Detection, Optimization, Graph ML, NLP Candidate must be comfortable in manipulating and analyzing complex, high dimensional data from various sources to solve difficult problems. Knowledge of working in cloud-computing environment like Azure / AWS / Google Cloud. Proficient in Relational Databases (SQL), Big Data Technologies (PySpark). Azure storage technologies such as ADLS, cosmos DB, etc., Generative AI experience is a plus * Bachelor's or higher degrees in Computer Science, Statistics, Mathematics, Engineering, or related disciplines.
