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The SightlineEnterprise Research and Detection team at HUMAN is dedicated to protecting the internet by disrupting the economics of cybercrime. Our team of Data Scientists research and develop algorithms to detect bots and fight fraud. We use statistical and machine learning techniques, in both offline and real-time infrastructures, to provide our customers with proactive security and insights into their attacks.
We’re looking for a Data Scientist to join our team to support the full-cycle of fraud fighting: from the early stages of gathering business requirements and exploratory research to the development and deployment in production of statistical detection techniques. This role is at the intersection of data, technology, and business as we aspire to deeply understand how actors are perpetrating schemes and develop a powerful product to delight our customers.
What you'll do
Partner with Product, Engineering, and Analysts to support our business goals in improving bot detection, building out competitive product features, and optimizing our data and ML infrastructure
Research and experiment with the latest detection frameworks in cyber security and fraud, statistical techniques, and ML tooling
Support the growth of best practices and Data Science standards in detection and modeling
Be an active participant in team responsibilities, including reviewing other data scientists’ work prior to launching new detection rules/algorithms, our ‘sheriffing’ process where we proactively monitor anomalies arising in our systems and taking part in ‘tech-debt days’ to keep our codebase clean and healthy
Contribute + develop software tooling and automation, that ‘creating tau’ (disproportionate leverage) by accelerating our team’s ability to find fraud, automating cumbersome tasks, and creating novel methodologies. by empowering the other humans around you
About HUMAN Security
HUMAN was founded in a Brooklyn sci-fi bookstore with a hacker mindset and the lofty mission of making the internet safer by putting humans first. Today, our Human Defense Platform safeguards enterprises from sophisticated bots, fraud, and account abuse, verifying the humanity of more than 20 trillion interactions per week for the world’s biggest brands and internet platforms.
HUMAN is an equal opportunity workplace dedicated to protecting the internet's integrity for everyone. We believe in putting people first, embracing diversity of thought from our global teams, and welcoming all individuals to share their unique experiences as we fight cybercrime together. We support our Humans with a comprehensive total rewards package for personal and professional development, including well-being and learning stipends, flexible work options, and dedicated time off. While our HQ is in NYC, we have teams worldwide.
HUMAN is growing fast, and there’s never been a more meaningful time to join us. If you thrive on solving complex problems and want to help shape the future of cybersecurity, join us as we build a safer, more human internet.
If you are an individual with a disability or special need that requires accommodation, please contact us directly.
Requirements
You have experience solving large-scale, data-intensive problems in production systems (we process trillions of events per day, so literacy with large datasets is a must!)
You can explain the nuances of different statistical modeling approaches and make informed decisions on which to use, whilst also having the engineering awareness of the realities of shipping working code to customers
You are fluent with Python and SQL and familiar with related tools, libraries and platforms.
You are fluent in Object Oriented development and have strong debugging skills
You have a good sense of ownership which drives you to find ways to do things better, faster, and leaner; you hold yourself accountable for the quality and accuracy of your work
You have experience working on cross-functional projects, with multiple stakeholders - both technical and non-technical; and following SDLC best practices: project management, version control, unit testing, and CI/CD