• Develop data solutions in collaboration with other team members and software engineering teams that meet and anticipate business goals and strategies • Work with senior data science engineers in analyzing and understanding all aspects of data, including source, design, insight, technology and modeling • Develop and manage scalable data processing platforms for both exploratory and real-time analytics • Oversee and develop algorithms for quick data acquisition, analysis and evolution of the data model to improve search and recommendation engines. • Document and demonstrate solutions • Design system specifications and provide standards and best practices • Support and mentor junior data engineers by providing advice and coaching • Make informed decisions quickly and taking ownership of services and applications at scale • Be a persistent, creative problem solver, constantly striving to improve and iterate on both processes and technical solutions • Remain cool and effective in a crisis • Understand business needs and know how to create the tools to manage them • Take initiative, own the problem and own the solution • Other duties as assigned

• Develop algorithms that transform raw, noisy, and unlabeled data into insights about browsers and devices • Design and implement supervised learning models to improve detection accuracy • Partner with ML Engineers, Data Scientists, and Software Engineers to push forward our technical expertise • Conduct exploratory data analysis to investigate anomalies and ad-hoc questions • Run experiments to solve ML engineering challenges like real-time inference and training automation

• Lead the full development cycle of machine learning models, from prototyping and feature engineering to validation and deployment, ensuring scalable and production-ready solutions. • Apply statistical techniques such as A/B testing and back-testing, especially in imbalanced classification problems where precision and recall are critical. • Support strategic initiatives such as detecting property undervaluation, process automation, and the creation of predictive tools for new business verticals. • Translate complex findings into clear, actionable insights, delivering results to technical and non-technical audiences, including C-level stakeholders. • Act as mentor and technical guide for the Data Science team, reviewing code, setting standards, and fostering a collaborative, high-performing culture.

• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics. • Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data). • Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse. • Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques. • Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning. • Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust. • Contribute to team standards for ML explainability, risk evaluation, and feature logging. • Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences. • Mentor junior data scientists and participate in cross-functional working groups.