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HUMAN (formerly clean.io) is a cybersecurity company protecting the internet by disrupting the economics of cybercrime. The SightlineEnterprise Research and Detection team develops algorithms to detect bots and fight fraud using statistical and machine learning techniques in both offline and real-time infrastructures.
You will join the Data Science team supporting the full cycle of fraud fighting: from gathering business requirements and exploratory research through development and production deployment of statistical detection techniques. This role sits at the intersection of data, technology, and business, requiring you to understand how threat actors perpetrate schemes and develop powerful products that delight customers.
Key responsibilities include:
- Partner with Product, Engineering, and Analysts to improve bot detection and build competitive product features
- Research and experiment with the latest detection frameworks in cybersecurity and fraud, statistical techniques, and ML tooling
- Support growth of best practices and Data Science standards in detection and modeling
- Participate in code reviews (the 'sheriffing' process) to monitor anomalies in production systems
- Contribute to tech-debt days to maintain codebase health
- Develop software tooling and automation that creates leverage by accelerating fraud detection, automating tasks, and creating novel methodologies
- Work cross-functionally with both technical and non-technical stakeholders
The role involves processing trillions of events per day, requiring strong literacy with large-scale datasets and production systems. You will work with Python, SQL, and related tools and platforms, applying statistical modeling and machine learning to real-world cybersecurity challenges.
HUMAN was founded with a hacker mindset to make the internet safer. The company verifies the humanity of more than 20 trillion interactions per week for major brands and internet platforms. Headquarters is in NYC with teams worldwide.
REQUIREMENTS:
- Experience solving large-scale, data-intensive problems in production systems
- Fluency with Python and SQL, plus familiarity with related tools, libraries, and platforms
- Fluency in Object-Oriented development with strong debugging skills
- Understanding of statistical modeling approaches and ability to make informed decisions on which techniques to use
- Engineering awareness of shipping working code to customers
- Experience working on cross-functional projects with multiple stakeholders
- Familiarity with SDLC best practices: project management, version control, unit testing, and CI/CD
- Strong sense of ownership and accountability for quality and accuracy of work
