Data Scientist II

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients' success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy.

Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower fearless commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here

Department: Applied Decision Science
The Applied Decision Science team drives client performance and ensures long-term stability. This involves a diverse range of responsibilities, from feature engineering and client-specific models that address unique regional or business needs, to adjusting thresholds and creating rules for optimal decisioning outcomes. We lead critical proof-of-value studies, conduct in-depth pricing analyses, and perform swift loss investigations to properly mitigate fraud attacks. We thrive on collaboration with the Risk Intelligence, Chargeback Investigation, and Product teams, and our contributions include pioneering novel modeling methods, advanced feature engineering, and robust mitigation management.

We expect our data scientists to be hands-on.  We carry solutions from a brainstorm to experimentation and all the way to deployment. 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.

We are looking for someone who embodies our company values:

Curious and Hungry:  Be willing to do research and design experiments by being hands-on

Tenacious: Creating something new is hard work, and our Data Scientist team never gives up

Customer Passion: Be the backbone to our platform, and help us stay ahead of fraudsters

Design for Scale: Work with the rest of the Data Science team to make fraud protection at scale possible

Agile: 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.

Roll Up Your Sleeves: Partner closely internally to learn from others, and succeed as a team

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:

• Bachelor's degree in computer science, applied mathematics, economics, or an analytical field
• An advanced degree (M.S. or Ph.D) in an analytical field is a plus
• At least 3+ years of experience
• 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
• Fluent in English

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
• Working directly with Go-to-Market teams 
• Previous work in fraud, payments, or e-commerce

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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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.

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