Staff Data Scientist - Digital Intelligence

The role

You will provide technical leadership for turning device, network, browser, mobile, API, and behavioral telemetry into production-grade fraud and identity risk signals. You will lead signal-development efforts, define evaluation methods, and influence telemetry collection to detect risky behavior.

What you will do

• Lead high-impact machine learning and feature-development initiatives.
• Own ambiguous fraud and identity risk problems.
• Develop production risk signals and models.
• Build and guide scalable feature-engineering approaches.
• Investigate complex signal patterns like spoofing and automation.
• Define evaluation methods for Digital Intelligence signals.
• Influence telemetry collection and production readiness.
• Translate product and fraud-risk questions into data science approaches.
• Raise team standards for feature quality and model validation.
• Communicate technical recommendations to stakeholders.

Required qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field.
• 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.
• Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems.
• Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
• Expert-level SQL skills.
• Extensive experience working with large-scale, complex, noisy datasets.
• Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools.
• Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation.
• Demonstrated ability to work with imperfect labels, delayed outcomes, telemetry artifacts, instrumentation gaps, and changing fraud patterns.
• Strong judgment across data quality, modeling approach, feature design, explainability, operational complexity, and business impact.
• Experience influencing data architecture, instrumentation, feature logging, and product direction through technical credibility.
• Excellent communication skills.

Nice to have

• Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, entity resolution, or graph-based risk signals.
• Experience designing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
• Experience with streaming, near-real-time, or low-latency decisioning systems.
• Familiarity with adversarial modeling, robust ML, privacy-preserving ML, interpretable ML, or responsible AI practices.
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
• Experience setting standards for model explainability, feature governance, validation methodology, or production ML observability.

About the company

Socure is building the identity trust infrastructure for the digital economy. The company provides digital identity verification and fraud prevention solutions using AI and machine learning.