Senior Data Scientist at Signifyd in Austin, TX
Data Science at Signifyd 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 skill sets through peer review of experiments and code, group paper study to deepen our ML and stats understanding, and frequent knowledge-sharing via live demos, write-ups, and special cross-team projects. The Data Science and Engineering teams at Signifyd have always had a strong contingent of remote folks, individual contributors as well as team leads. The challenges of working remotely aren't new to us and we have a track record of iterative improvements to our remote culture. Here you'll have the opportunity to:
Build production machine learning models that stop fraud rings Think creatively to engineer new features that identify fraudulent behavior Devise algorithmic approaches to payments risk management that evolve the process from one that's human-driven and heavy on gut feel to one that is quantitatively-rigorous and leverages an ecosystem of in-house tools Work collaboratively with other teams across the company on strategies to tackle entirely new e-commerce verticals, geographical regions, and product offerings Past experience you'll need:
A degree in computer science or a comparable quantitative field At least 5 years of post-undergrad work experience Building production machine learning models (they don't need to have been related to fraud) Hands-on statistical analysis with a solid fundamental understanding Writing code and reviewing others' in a shared codebase, preferably in Python Practical SQL knowledge Familiarity with the Linux command line Bonus points if you have:
Previous work in fraud, payments, or e-commerce A Master's Degree or PhD
Salary Range:
$80K -- $100K
Minimum Qualification
Data Science & Machine LearningEstimated Salary: $20 to $28 per hour based on qualifications.
Build production machine learning models that stop fraud rings Think creatively to engineer new features that identify fraudulent behavior Devise algorithmic approaches to payments risk management that evolve the process from one that's human-driven and heavy on gut feel to one that is quantitatively-rigorous and leverages an ecosystem of in-house tools Work collaboratively with other teams across the company on strategies to tackle entirely new e-commerce verticals, geographical regions, and product offerings Past experience you'll need:
A degree in computer science or a comparable quantitative field At least 5 years of post-undergrad work experience Building production machine learning models (they don't need to have been related to fraud) Hands-on statistical analysis with a solid fundamental understanding Writing code and reviewing others' in a shared codebase, preferably in Python Practical SQL knowledge Familiarity with the Linux command line Bonus points if you have:
Previous work in fraud, payments, or e-commerce A Master's Degree or PhD
Salary Range:
$80K -- $100K
Minimum Qualification
Data Science & Machine LearningEstimated Salary: $20 to $28 per hour based on qualifications.
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