Analyst, Merchant Solutions

Signifyd helps businesses of all sizes minimize their fraud exposure and grow their sales. Signifyd improves the e-commerce shopping experience for everyone by reducing the number of false positive declines of good buyers and by making fraud less profitable for criminals.

The Data Science organization at Signifyd is responsible for building, maintaining, and monitoring production ML models and risk management tools that are the core of Signifyd's product. The Merchant Solutions team within Data Science maintains a balance between custom and scalable solutions for Signifyd's customers. As an Analyst, you will be part of the Merchant Solutions team within the Data Science organization, helping to ensure solutions are achieving their desired results.

In terms of culture, we value collaboration and team ownership -- no one should feel they're solving a hard problem alone. Working across job functions, team boundaries, and hierarchies is not only encouraged, but is required to be successful at Signifyd. We're all in the same boat, and value team members that both seek to influence the direction of travel, and actively contribute to helping Signifyd improve the e-commerce shopping experience.

A typical day: Use a combination of data engineering, science, and analytics based skills to build business insights regarding merchant performance at Signifyd. Utilize a combination of in house developed tooling and adhoc analyses to identify opportunities and evaluate merchant performance. Developing dashboards, conducting analysis in notebooks, querying relational databases, and investigating fraud patterns to help manage merchant performance. Collaborate with data scientists to understand the reasoning behind model decisions, root causing incorrect decisions, and developing ideas to improve model decisions. Past experience you'll need: A degree in a STEM related field, or equivalent work experience 2+ years of post-undergrad work experience Strong verbal and written communication skills Expert SQL knowledge and practical Python experience for data analysis Experience building dashboards with software such as Looker, Tableau, etc. Translating ambiguous problem statements into actionable analyses Influencing peers through storytelling with data Bonus points if you have: Previous work in fraud, payments, or e-commerce Data analysis in a distributed environment Familiarity with the Linux command line and version control

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