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Socure is building identity trust infrastructure for the digital economy, using AI and machine learning to verify identities and prevent fraud in real time. The company serves businesses, governments, and millions of end users globally.
In this Senior Data Scientist role on the Digital Intelligence team, you will design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention. You'll work with high-volume, rich data from browser, mobile, and API traffic to surface meaningful risk signals and insights.
Key responsibilities include:
- Design and deploy ML systems balancing precision, recall, and adversarial dynamics
- Build scalable data pipelines and production ML workflows using structured and unstructured telemetry
- Investigate complex signals (emulator use, spoofing, low-entropy fingerprints) using advanced statistical methods
- Translate ambiguous business problems into modeling approaches using supervised, unsupervised, and heuristic techniques
- Partner with engineering, product, and risk teams on data architecture, signal collection, and planning
- Drive experimental design, A/B testing, and validation to ensure model generalizability
- Contribute to team standards for ML explainability and risk evaluation
- Communicate results through dashboards, presentations, and reports for technical and executive audiences
- Mentor junior data scientists and participate in cross-functional working groups
You bring 6+ years of data science or applied ML experience in production environments, with a Master's degree (or equivalent) in Computer Science, Machine Learning, Statistics, or related field. You have excellent SQL skills, extensive experience with large-scale databases, and proficiency in Python and distributed computing (Spark, PySpark). You're hands-on with ML frameworks (scikit-learn, XGBoost, TensorFlow) and experienced deploying and maintaining models in live systems, ideally with streaming or near-real-time data. You excel at designing experiments, working with noisy datasets, and applying sound validation techniques. Strong communication skills and ability to work cross-functionally with product, engineering, and analytics teams are essential.
Preferred: background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling; experience with high-cardinality feature engineering; familiarity with privacy-preserving ML; knowledge of browser/mobile fingerprinting or VPN/proxy detection.
