Staff Data Scientist

<h2><strong>About Us</strong></h2> <p>Riskified empowers businesses to unleash ecommerce growth by taking risk off the table. Many of the world’s biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists and researchers, Riskified’s AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights.</p> <p>Riskified is proud to work with incredible companies in virtually all industries including Booking.com, Acer, Gucci, Lorna Jane, GoPro, and <a href="https://www.riskified.com/customers/">many more</a>.</p> <p>We thrive in a collaborative work setting, alongside great people, to build and enhance products that matter. Abundant opportunities to create and contribute provide us with a sense of purpose that extends beyond ourselves, leaving a lasting impact. These sentiments capture why we choose Riskified every day. </p> <p> </p> <h2><strong>About the Role</strong></h2> <p>The Data Science department powers Riskified’s core engine by building production-grade algorithmic solutions at immense scale. As a Staff Data Scientist on the Identity team, you will drive the innovation roadmap for our Identity Graph Engine. You will architect, research, and deploy state-of-the-art machine learning models - spanning graph neural networks, transformers, and deep tabular architectures - to solve complex entity resolution and fraud prevention challenges at scale.</p> <h2><strong>What You'll Be Doing</strong></h2> <ul> <li><strong>Drive Identity Graph Innovation:</strong> Architect and advance the next generation of our Identity Graph Engine using state-of-the-art graph models, transformers, and machine learning techniques.</li> <li><strong>Technical Leadership:</strong> Define the research roadmap for identity resolution, setting technical standards and guiding complex research projects from initial concept to high-throughput production systems.</li> <li><strong>Large-Scale Modeling:</strong> Build scalable predictive and representation-learning models over massive tabular, sequence, and graph-structured datasets.</li> <li><strong>Cross-Functional Architecture:</strong> Collaborate closely with Data Engineering, Core Software, and Product teams to seamlessly integrate complex ML pipelines into real-time production environments.</li> <li><strong>Mentorship & SOTA Research:</strong> Benchmark and import cutting-edge academic research into practica