Senior Data Scientist, Signifyd

• A degree in computer science or a comparable analytical field
• 5+ years of post-undergrad work experience required
• Experience leading projects
• Strong verbal and written communication skills
• Strong machine learning and statistical background and a track record of being able to deliver under pressure
• Write code and review others' in a shared codebase in Python
• Practical SQL knowledge
• Design experiments and collect data
• Familiarity with the Linux command line
• A Master's Degree or PhD

Desirable

• The Data Science team builds production machine learning models that are the core of Signifyd's product
• We help businesses of all sizes minimize their fraud exposure and grow their sales
• We also improve the e-commerce shopping experience for individuals by reducing the number of folks' orders that are incorrectly declined and by making account hijacking less profitable for criminals
• The team has end-to-end ownership of our decisioning engine, from research and development to online performance and risk management
• We value collaboration and team ownership -- no one should feel they're solving a hard problem alone
• Together we help each other develop our skills through peer review of experiments and code, group paper study to deepen our ML and stats understanding, and frequent knowledge-sharing through live demos, write-ups, and special cross-team projects
• The Data Science and Engineering team at Signifyd have always had a strong contingent of remote folks, individual contributors and team leads
• The challenges of working remotely aren't new to us and we have experienced iterative improvements to our remote culture
• Be directly responsible for the performance of enterprise merchants with household names
• Research real-time new fraud patterns with our Risk Intelligence team
• Improve the important components of the Signifyd Commerce Protection Platform
• Communicate complex ideas to a variety of audiences, including executives
• Build production machine learning models that identify fraud
• Write production and offline analytical code in Python
• Work with distributed data pipelines
• Collaborate with engineering teams to strengthen our machine-learning pipeline