Senior Data Scientist - Digital Intelligence, Device Signals

Why Socure?Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.About the RoleSocure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.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.What You'll DoDesign 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.What You BringMaster’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.Preferred QualificationsBackground in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).Familiarity with privacy-preserving or robust ML techniques.Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.What You’ll GainHands-on experience with real-world data science challenges in a high-impact industry.A collaborative and inclusive work environment that fosters learning and growth.Opportunities to grow into staff-level or technical leadership roles over time.Socure