Data Scientist

About Riskified

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, fraud prevention, and policy abuse at scale. The company's AI-powered fraud and risk intelligence platform analyzes individual interactions to provide real-time decisions and robust identity-based insights. Riskified works with leading companies including Acer, Gucci, Lorna Jane, and GoPro.

About the Role

The Data Science department generates value by developing algorithms and analytical production-grade solutions. As a Data Scientist, you will work end-to-end on project development and implementation, leveraging advanced techniques including classification models, NLP, anomaly detection, graph theory, and deep learning. This role requires strong quantitative and analytical skills, solid background in statistical modeling and machine learning, and a passion for driving data-driven decision-making.

Key Responsibilities

• Data Exploration & Preprocessing: Collect, clean, and transform large, complex datasets from various sources to ensure data quality and integrity
• Statistical Analysis & Modeling: Apply statistical methods and mathematical models to identify patterns, trends, and relationships; develop predictive models
• Machine Learning: Develop and implement algorithms including classification, regression, clustering, and deep learning to solve business problems
• Feature Engineering: Extract relevant features from structured and unstructured data; design new features to enhance model performance
• Model Development & Evaluation: Build, train, and optimize machine learning models using state-of-the-art techniques; evaluate performance using appropriate metrics
• Data Visualization: Present complex analysis results clearly using data visualization techniques; communicate insights to stakeholders
• Cross-Functional Collaboration: Work with cross-functional teams to solve complex business problems and drive impact

What We're Looking For

• Strong foundation in statistics, machine learning, and mathematical modeling
• Experience with data exploration, preprocessing, and feature engineering
• Proficiency in Python, SQL, or similar data science tools
• Ability to communicate complex findings to non-technical stakeholders
• End-to-end project ownership mindset