*This position requires you to be located in the US

*Must be willing to work EST hours 

The Data Science team at Signifyd builds the models that power our fraud detection engine.  We make fraud prevention at scale possible.  Our machine learning pipeline keeps us one step ahead of fraudsters and their constantly evolving tactics and our research and experiments develop into new products that improve the merchant payments experience.

We expect our data scientists to be hands-on.  We carry solutions from a brainstorm to experimentation and all the way to deployment.  Some days you may spend doing research and designing experiments while others are spent using your analytical toolbox to surface insights into real-time fraud attacks.  We’re a varied group with a diversity of strengths -- some team members came to us from academic backgrounds, others from engineering, some from big companies and some from small, but all of us are curious and collaborative.

How you’ll have an impact:

• Building production machine learning models that identify fraud
• Designing new algorithms that optimize all the key components of the Signifyd Commerce Protection Platform
• Writing production and offline analytical code in Python and Java
• Researching real-time emerging fraud patterns with the Risk Analysis team
• Working with distributed data pipelines
• Communicating complex ideas effectively to a variety of audiences
• Collaborating with engineering teams to continuously strengthen our machine learning pipeline
• Mentoring other members of the team

Past experience you’ll need:

• An advanced degree (M.S. or Ph.D) in computer science, applied mathematics, or a comparable analytical field
• At least 2-3 years of experience post M.S. required
• Hands-on statistical analysis with a solid fundamental understanding
• Designing experiments and collecting data
• Writing code and reviewing others’ in a shared codebase, preferably in Python and Java
• Practical SQL knowledge
• Familiarity with the Linux command line

Experience we love to see:

• Data analysis in a distributed environment
• Passion for writing well-tested production-grade code
• Using visualizations to communicate analytical results to stakeholders outside your team
• Previous work in fraud, payments, or e-commerce