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:

  • Be directly responsible for the performance of thousands of merchants
  • Research real-time new fraud patterns with our Risk Intelligence team
  • Build production machine learning models that stop fraud rings
  • Think creatively to engineer new features that identify fraudulent behavior
  • Write production and offline code in Python, SQL, and Java to analyze and build tools that allow us to scale
  • 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

Past experience you’ll need:

  • A degree in computer science or comparable educational bootcamp
  • 4+ years of post-undergrad work experience
  • Experience leading projects
  • 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
  • Designing experiments and collecting data
  • 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

Benefits in the UK:

  • 4-day workweek
  • A competitive base salary
  • An equally competitive equity package
  • Annual Performance Bonus or Commissions
  • Pension matched up to 8%
  • ‘Day one’ access to great health, dental and optical insurance scheme 
  • 20 days of annual leave plus public holidays
  • Cycle to Work Scheme
  • Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads, plus 12 weeks half-pay for mums)
  • Regular paid social events organized by our social committee 
  • Mental wellbeing resources
  • Dedicated learning budget through Learnerbly

We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships.


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