Description:

The Data Science team at Signifyd builds the models that power the fraud detection engine.
The machine learning pipeline is designed to stay ahead of fraudsters and their evolving tactics.
Research and experiments conducted by the team develop into new products that enhance the merchant payments experience.
Data scientists are expected to be hands-on, carrying solutions from brainstorming to experimentation and deployment.
The team is diverse, with members from various backgrounds including academia, engineering, and different company sizes.
The company values include being curious and hungry, tenacious, passionate about customers, designing for scale, being agile, and rolling up sleeves to collaborate.

Requirements:

A Bachelor's degree in computer science, applied mathematics, economics, or an analytical field is required.
An advanced degree (M.S. or Ph.D.) in an analytical field is a plus.
At least 3+ years of experience in a relevant field is necessary.
Hands-on statistical analysis experience with a solid fundamental understanding is required.
Experience in designing experiments and collecting data is essential.
Proficiency in writing code and reviewing others’ code in a shared codebase, preferably in Python and Java, is needed.
Practical knowledge of SQL is required.
Familiarity with the Linux command line is necessary.
Fluency in English is required.

The position offers the opportunity to build production machine learning models that identify fraud.
There is a chance to design new algorithms that optimize key components of the Signifyd Commerce Protection Platform.
The role involves writing both production and offline analytical code in Python and Java.
Data scientists will research real-time emerging fraud patterns with the Risk Analysis team.
Collaboration with engineering teams to strengthen the machine learning pipeline is encouraged.
Mentoring opportunities for other team members are available.