Senior Data Scientist, Socure

• Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field
• 6+ years of experience in data science or applied machine learning, including experience working in production environments
• Excellent SQL skills and extensive experience with large-scale databases and data modeling
• Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data
• Proficiency in Python and distributed computing tools (e.g., Spark, PySpark)
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar
• Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership
• Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness
• Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies
• Strong judgment across data quality, model selection, and business impact tradeoffs
• Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams

Desirable

• We are seeking a Senior Data Scientist to join our Digital Intelligence team
• In this role, you will drive the development of machine learning features and models that leverage device, network, and behavioral data to power fraud prevention and identity verification
• You’ll work with rich, high-volume data from browser, mobile, and API traffic to surface meaningful insights and scalable risk signals
• This is a great opportunity to own impactful projects, collaborate cross-functionally, and deepen your expertise in applied ML for device and behavioral intelligence
• 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