The Data Science team builds production machine learning models and fraud mitigation tools 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, from individual contributors up to the executive level.  The challenges of working remotely aren’t new to us and we have a track record of iterative improvements to our remote culture.

You’ll be on a horizontally-focused team that works on tooling for data scientists in order to help the organization fight fraud attacks and scale. You will be leading and planning data- and algorithmically-driven tools and helping test whether these strategies are effective. 

Here you’ll have the opportunity to:

• Concept, plan, and develop tools and strategies to stop fraud attacks
• Write production and offline code in Python and SQL to analyze and build tools that allow us to scale
• Think creatively to engineer new analyses, algorithms, and models 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
• Build production machine learning models that stop fraud rings
• Research real-time new fraud patterns with our Risk Intelligence team

Past experience you’ll need:

• A degree in an analytical field, preferably with a computer science component
• 4+ years of post-undergrad work experience
• Experience leading and technically planning projects
• Writing well-tested production-grade code and reviewing others’ in a shared codebase, preferably in Python
• Experience with pandas, sklearn, numpy and other core Python data science libraries
• Building production machine learning models (they don’t need to have been related to fraud)
• Hands-on statistical analysis with a solid fundamental understanding
• Practical SQL knowledge
• Familiarity with the Linux command line

Bonus points if you have:

• Previous work in fraud, payments, or e-commerce
• Passion for writing well-tested production-grade code
• A Master's Degree or PhD

Check out how Data Science is powering the new era of Ecommerce

Check out our Director of Data Science featured in Built In

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Benefits in our US offices:

• 4-day workweek
• Discretionary Time Off Policy (Unlimited!)
• HeadSpace Health Online Therapy Membership
• Dedicated learning budget through Learnerbly
• 401K Match
• Stock Options
• Annual Performance Bonus or Commissions
• Paid Parental Leave (12 weeks)
• Health Insurance
• Dental Insurance
• Vision Insurance
• Flexible Spending Account (FSA)
• Short Term and Long Term Disability Insurance
• Life Insurance
• Company Social Events
• Signifyd Swag