Senior Data ScientistApply now
Signifyd leads the world in bringing the insights, innovation and compassion required to foster fearless commerce in a time of increasing digital threats. Working with some of the industry’s most recognizable retailers and brands, we are focused on using technology to enhance customer lifetime value and protect enterprises from fraud so they can focus on growing their business.
We process billions in ecommerce transactions annually through our Commerce Network of thousands of merchants selling in more than 100 countries. We focus every day on harnessing machine learning and artificial intelligence in more powerful ways to maximize our customers’ revenue and their security. None of that happens without the right people.
Our team’s strength is in its diversity and its acceptance of new ideas and new ways to look at old challenges. We are dedicated disruptors designing a new world of commerce at scale. We know humans are not one-dimensional and we celebrate the uniqueness each individual brings to the problems we solve and the culture we create.
Data Science at Signifyd
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 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.
Here you’ll have the opportunity to:
- Build production machine learning models that stop fraud rings
- Think creatively to engineer new features that identify fraudulent behavior
- Devise algorithmic approaches to payments risk management that evolve the process from one that’s human-driven and heavy on gut feel to one that is quantitatively-rigorous and leverages an ecosystem of in-house tools
- Work collaboratively with other teams across the company on strategies to tackle entirely new e-commerce verticals, geographical regions, and product offerings
- Mentor other team members and help to foster a psychologically-safe environment that allows everyone on the team to leverage their full creativity and grow professionally
Past experience you’ll need:
- A degree in computer science or a comparable quantitative field
- At least 5 years of post-undergrad work experience
- Building production machine learning models (they don’t need to have been related to fraud)
- Hands-on statistical analysis with a solid fundamental understanding
- Writing 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
- A Master's Degree or PhD
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