PLEASE APPLY IN ENGLISH
The Data Science team builds production machine learning models that are the core of Signifyd's product.
Our product helps businesses of all sizes minimize their fraud exposure and grow their sales. This translates into improved e-commerce shopping experience for individuals, by reducing the number of orders that are incorrectly declined, and by making account hijacking less profitable for criminals.
The data science team has end-to-end ownership of our decision 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 grow our skills through peer reviews, group studies, and frequent knowledge sharing to deepen our ML and stats understanding. This is done through 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 and team leads. The challenges of working remotely aren't new to us and we strive to iteratively improve our remote culture.
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
- Writing production and offline analytical code in Python
- Working with distributed data pipelines
- Communicating complex ideas effectively to a variety of audiences
- Collaborate with engineering teams to strengthen our machine-learning platform
Requirements:
- Bachelor's degree in computer science, applied mathematics, economics, or an analytical field or equivalent practical experience
- At least 5+ years of experience
- Building production ML models
- 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
- Practical SQL knowledge
- Familiarity with the Linux command line
- Fluent in English
- This role has on-call shifts, as part of our weekend rotation, Fri/Sat/Sun. While the number of shifts is subject to change, currently it works out to about six weekends a year.
Bonus points if you have
- Advanced degree in an analytical field (Master, PHD)
- Previous work in fraud, risk, payments, or e-commerce
- Worked previously with Go-to-Market teams directly
- Data analysis experience in a distributed environment
- Passion for writing well-tested production-grade code
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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