Senior Applied Data Scientist

Summary

Join Abnormal Security as an Applied Data Scientist on the Message Detection - Attack Detection team, where you'll play a crucial role in building a high-recall detection engine to protect customers from evolving cyber threats. You'll analyze false negatives and positives, understand features that distinguish safe emails from attacks, train models, and develop detectors to improve detection efficacy. You'll also identify and recommend new features and ML model approaches to enhance the product's effectiveness. This role involves working with infrastructure and systems engineers to productionize signals, writing testable and readable code, and actively monitoring and improving FN rates and efficacy rates. You'll also contribute to other areas of the stack, such as building and debugging data pipelines or presenting results to customers.

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, and 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, through  feature engineering, rules and ML modeling
• 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

Preferred Qualifications

• MS degree in Computer Science, Electrical Engineering or other related engineering/applied Sciences field
• Experience with algorithms and optimization