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Socure is 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.
Job Responsibility:
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
Requirements:
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
Nice to have:
Background 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 we offer:
Offers Equity
Offers Bonus
total rewards package includes equity, benefits, and an annual bonus or a commission plan
A collaborative and inclusive work environment that fosters learning and growth
Opportunities to grow into staff-level or technical leadership roles over time