Staff Data Scientist, Signifyd

• A degree in computer science or a comparable analytical field
• 7+ years of post-undergrad work experience with production-grade ML
• Deep ML expertise with a solid foundation in machine learning, experimentation, and model evaluation techniques
• Proven experience leading technical initiatives and influencing decision-making across teams
• Strong communication skills– You are great at breaking down complex ML and DS topics to non-technical audiences. You are also great at using visualization to communicate analytical results
• Strong product & business mindset with a passion for applying data science to solve real-world problems with measurable outcomes
• Proficiency in Python, SQL, and key ML libraries

Desirable

• The Data Science organization at Signifyd is responsible for building, maintaining, and monitoring production ML models and risk management tools that are the core of Signifyd's product
• The Staff Data Scientist will be part of the Core ML team within the Data Science organization
• The team owns the core fraud detection model that decides the majority of the traffic, as well as our model training and evaluation infrastructure
• We work closely with the ML and Data Platform teams
• The team contributes to: novel modeling methods, advanced feature engineering, and robust statistical practices for offline and online evaluation
• Signifyd is looking to add a few Staff Data Scientists to our team
• As a Staff Data Scientist, you’ll play a pivotal role in shaping the future of our machine learning capabilities
• You won’t just build models—you’ll influence strategy, mentor teams, and drive measurable improvements in our fraud detection systems
• You’ll be responsible for solving critical challenges in model performance measurement, accelerating experimentation, and pioneering new ML technologies to stay ahead of evolving fraud tactics
• Expand ML Capabilities – Identify, prototype, and integrate new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability
• Drive Experimentation at Scale – Develop and implement robust experimentation frameworks to increase velocity while maintaining scientific rigor, enabling rapid iteration and deployment of fraud detection models
• Architect & Optimize ML Pipelines – Design and build scalable, end-to-end ML pipelines that support both offline and online measurement of model performance, ensuring our models deliver real-world impact
• Build Influence Across Teams – Collaborate closely with engineering, product, and risk teams to align ML architecture and experimentation with business goals, driving measurable outcomes
• Lead & Mentor – Lead solving the technical concerns for the entire organization and elevate the technical bar by mentoring data scientists and engineers, sharing best practices, and fostering a culture of innovation and excellence in ML practices