SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Signifyd is a fraud prevention and risk management platform trusted by thousands of merchants across 100+ countries. The Applied Decision Science (ADS) team builds production machine learning models and risk management tools that form the core of Signifyd's product, helping businesses minimize fraud exposure while improving the e-commerce experience for legitimate customers.
As a Data Scientist II, you will be a full-stack operator responsible for end-to-end development, deployment, and evaluation of ML solutions. You'll partner with Business Unit Leads to identify gaps in decisioning performance and implement solutions with guidance from senior team members. Key responsibilities include building and improving production ML models that identify fraud, designing and running experiments to optimize decisioning strategies, automating repetitive manual work, and communicating complex technical ideas to diverse audiences including Customer Success, Sales, and external customers.
You'll work with Python for production and analytical code, distributed data pipelines using Spark/Databricks/GCP, and SQL for data analysis. The role emphasizes collaboration and peer learning—all team members are expected to review and weigh in on colleagues' work regardless of level. The team values full-stack thinking, understanding how transaction data flows through the system and into models.
Signifyd's culture is heavily remote-first with most ICs and leaders working remotely. The company uses Slack extensively and embraces generative AI tools for productivity. Teams gather once yearly with no travel requirement. The role includes on-call shifts as part of a weekend rotation (approximately six weekends per year across Friday/Saturday/Sunday).
Required: Bachelor's degree in computer science or comparable analytical field; 3+ years post-undergrad work experience; strong ML and statistical background; proficiency in Python, SQL, and code review; experience with distributed analytics tools like Spark and Databricks; ability to design experiments and collect data; strong communication skills.
Nice-to-have: background in fraud, payments, or e-commerce; distributed data analysis experience; passion for production-grade code; experience with AI coding agents; A/B testing in production; advanced degree.
