sift - Data Scientist
Requirements
• Experience in fraud, risk, trust and safety, cybersecurity, or adjacent domains. • Familiarity with Java or another object-oriented programming language. • Experience partnering closely with software engineers to productionize analytical or machine learning improvements. • Please note: Final round interviews may be held in person • Let’s build it together: • At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet. • This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
Responsibilities
• Analyze fraud patterns, customer behavior, and model outcomes to identify opportunities for product and model improvements. • Partner with engineering and product teams to define evaluation metrics, investigate gaps in current product behavior, and propose practical improvements that drive customer value. • Design and run experiments on features, modeling approaches, and datasets to validate ideas and improve model performance. • Evaluate model quality through dataset analysis, error analysis, calibration, and score distribution investigations. • Build repeatable analyses, prototypes, and internal tools that support research, diagnosis, and operational decision-making. • Communicate findings and recommendations clearly across data science, engineering, and business stakeholders. • WHAT WILL MAKE YOU A STRONG FIT • Bachelor’s degree in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience. • 2+ years of relevant industry experience in data science, machine learning, analytics, or a closely related field. • Strong foundation in machine learning and data science best practices, with experience applying them to real-world problems. • Experience working with large datasets using tools such as Python, Jupyter, Pandas, PySpark, scikit-learn, PyTorch, TensorFlow, or similar technologies. • Comfort performing both deep analysis and lightweight prototyping to test ideas quickly. • Strong problem-solving skills and the ability to work effectively in ambiguous spaces with competing priorities. • Clear communication and collaboration skills, with a team-first mindset.
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