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 Data Science 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.

You’ll be on a horizontally-focused team that’s laser-focused on stopping fraud in its tracks. You will be leading and planning algorithmically-driven solutions, testing whether and how those strategies will be effective, and getting them into production. 

 

Here you’ll have the opportunity to:

  • Concept, plan, and develop strategies for identification and remediation of fraud attacks. 
  • Devise algorithmic approaches with the right evaluation frameworks, and how to apply them in end-to-end systems 
  • Build production machine learning models that stop fraud rings
  • Write production and offline analytical code in Python, SQL, and Java
  • Collaborate with engineering teams to strengthen our machine-learning platform
  • Communicate complex ideas to a variety of audiences

Past experience you’ll need:

  • A degree in computer science or a comparable quantitative field
  • 4+ years of post-undergrad work experience
  • Experience leading projects, designing experiments, and collecting data
  • Building production machine learning models (they don’t need to have been related to fraud)
  • Experience with graph-based frameworks, spark, pandas, sklearn, numpy, and other core Python DS libraries
  • Hands-on statistical analysis with a solid fundamental understanding
  • Writing production 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
  • 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

#LI-Remote

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

We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.

Signifyd provides a base salary, bonus, equity and benefits to all its employees. Our posted job may span more than one career level, and offered level and salary will be determined by the applicant’s specific experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data.

USA Base Salary Pay Range
$135,000$160,000 USD

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