About the job
At Signifyd, we empower merchants to thrive by fostering trustworthy relationships with their customers. Our cutting-edge technology, coupled with a team that's genuinely dedicated to our clients' success, creates seamless shopping experiences that approve more legitimate orders, safeguard revenue, and enhance customer satisfaction.
Trusted by thousands of top merchants across over 100 countries, we securely handle billions of transactions annually. Our people are at the core of our mission, propelling us forward with dedication, empathy, and creativity. Join us in our quest to enable fearless commerce by assisting online retailers in delivering exceptional customer experiences and combatting fraud. Discover our company values here!
Signifyd’s Machine Learning team develops and maintains production ML models and risk management tools that are fundamental to our offerings. These models are a crucial aspect of our entire product suite.
We assist businesses of all sizes in minimizing fraud risk while boosting their sales. By reducing false positive declines of legitimate buyers, we enhance the e-commerce experience for everyone and make fraud less rewarding for criminals.
The team takes complete ownership of our decision-making engine, overseeing everything from research and development to online performance and risk management.
We cherish collaboration and team ownership—no one should feel isolated while tackling challenging problems.
Together, we support each other in developing our skills through peer reviews of experiments and code, group discussions to deepen our understanding of ML and statistics, and regular knowledge-sharing through live demonstrations, write-ups, and collaborative projects.
Your Impact:
- Investigate emerging fraud patterns in real-time alongside our Risk Intelligence team.
- Enhance key components of the Signifyd Commerce Protection Platform.
- Articulate complex concepts to diverse audiences, including executives.
- Develop production machine learning models to detect fraud.
- Write production and offline code using Python and PySpark.
- Work with distributed data pipelines.
- Collaborate with engineering teams to reinforce our machine-learning pipeline.
Required Experience:
- A degree in computer science or a related analytical field.
- 3+ years of professional experience in machine learning or a related domain.
- Strong programming skills in Python and experience with machine learning frameworks.
- Familiarity with data processing and distributed systems.

