The leading provider of digital identity verification and fraud solutions. Salesinfo@socure.com
Senior Data Scientist – Digital Intelligence, Device Signals
Location
United States
Posted
72 days ago
Salary
$150K - $185K / year
Seniority
Senior
Job Description
Senior Data Scientist – Digital Intelligence, Device Signals
Socure
• 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.
Job 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.
Benefits
- Offers Equity
- Offers Bonus
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