- Location
- Budapest, Hungary; · Budapest, Budapest, Hungary
- Workplace
- Remote, Hybrid
- Department
- Machine Learning Engineering
- Education
- PhD
- Source
- Greenhouse
Description
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!
Signifyd’s Machine Learning team builds production ML models and risk management tools that are the core of Signifyd's product. These models are an integral part of all our products.
We help businesses of all sizes minimize their fraud exposure and grow their sales. We improve 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 team has end-to-end ownership of our decision-making 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 skillsets through peer review of experiments and code, group paper study to deepen our ML and stats understanding, and frequent knowledge-sharing through live demos, write-ups, and special cross-team projects.
How you'll have an impact:
- Research emerging fraud patterns in real-time with our Risk Intelligence team
- Improve the important components of the Signifyd Commerce Protection Platform
- Communicate complex ideas to a variety of audiences, including executives
- Build production machine learning models that identify fraud
- Write production and offline code in python, PySpark
- Work with distributed data pipelines
- Collaborate with engineering teams to strengthen our machine-learning pipeline
Past experience you'll need:
- A degree in computer science or a comparable analytical field
- 3+ years of post-undergrad work experience required
- Strong verbal and written communication skills
- Strong machine learning and statistical background, and a track record of being able to deliver under pressure.
- Write code and review others' in a shared codebase in Python
- Practical SQL knowledge
- Design experiments and collect data
- Familiarity with the Linux command line
Bonus points if you have:
- Previous work in fraud, payments, or e-commerce
- Data analysis in a distributed environment
- Passion for writing well-tested production-grade code
- A Master's Degree or PhD
Benefits:
- Stock Options
- Annual Performance Bonus or Commissions
- Pension matched up to 3%
- ‘Day one’ access to great health insurance scheme
- Paid team social events
- Mental wellbeing resources
- Dedicated learning budget through Learnerbly
