Senior Data Scientist - Digital Intelligence, Device Signals

The role

You will drive the development of machine learning features and models using device, network, and behavioral data. You will work with high-volume data from browser, mobile, and API traffic to surface risk signals.

What you will do

• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention.
• Contribute to the development of scalable data pipelines and production ML workflows.
• Investigate high-complexity signals using advanced statistical methods.
• Translate ambiguous business problems into modeling approaches.
• Partner with engineering, product, and risk teams on data architecture and signal collection.
• Drive experimental design, A/B testing frameworks, and validation techniques.
• Contribute to team standards for ML explainability, risk evaluation, and feature logging.
• Document methodologies and communicate results through dashboards, presentations, and reports.
• Mentor junior data scientists and participate in cross-functional working groups.

Required qualifications

• 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.
• Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques.
• Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights.
• 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.

Benefits

• Hands-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.

About the company

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