Senior Applied Data Scientist

Summary

Join Abnormal Security as an Applied Data Scientist to contribute to building a high-recall detection engine for message attacks, utilizing machine learning and behavioral AI.

Requirements

• 5+ years experience designing, building product machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search
• Experience with data analytics and wielding SQL+pandas framework to both build metric and evaluation pipelines, and answer critical questions about counterfactual treatments
• Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model / system that can accomplish the goal
• Ability to rapidly iterate on 0-to-1 model prototypes, interpret results, and pivot an approach, in order to evaluate most promising solutions as new problems arise
• Uses a systematic approach to debug data issues within both ML and heuristics models
• Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow
• Effective programming skills which enable them to quickly add incremental logic to our codebase with readable, well tested and efficient code
• BS degree in Computer Science, Applied Sciences, Information Systems or other related engineering field

Responsibilities

• Deep inspection and row level data analysis of our false negatives and false positives
• Produce data and feature insights to iteratively improve our detection efficacy
• Understand features that distinguish safe emails from email attacks, and utilize them effectively into our models stack and engine
• Train models and develop detectors on well-defined datasets to improve model efficacy on specialized attacks
• Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product
• Work with infrastructure & systems engineers to productionize signals to feed into the detection system
• Writes code with testability, readability, edge cases, and errors in mind
• Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories
• Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises