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SeniorFull-timePythonRustMachine LearningData ScienceTensorFlowGitAIAPIML
Salary Range
$143,650 - $194,350 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
Socure is seeking a Senior Data Scientist to join their Digital Intelligence team. This role involves developing machine learning features and models that leverage device, network, and behavioral data to power fraud prevention and identity verification.
Key Responsibilities
- →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 using structured and unstructured telemetry
- →Investigate high-complexity signals, applying advanced statistical methods and domain knowledge to detect fraud and abuse
- →Translate ambiguous business problems into modeling approaches using supervised, unsupervised, and heuristic techniques
- →Partner with engineering, product, and risk teams to contribute to data architecture decisions
- →Drive experimental design, A/B testing frameworks, and robust validation techniques
- →Contribute to team standards for ML explainability, risk evaluation, and feature logging
- →Mentor junior data scientists and participate in cross-functional working groups
Requirements
- ✓Master’s degree in Computer Science, Machine Learning, Statistics, or a related quantitative field
- ✓6+ years of experience in data science or applied machine learning 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
- ✓Proficiency in Python and distributed computing tools like Spark and PySpark
- ✓Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow
- ✓Excellent communication skills to explain complex technical results to non-technical stakeholders
- ✓Experience designing and interpreting experiments with real-world noisy datasets
- ✓Demonstrated ability to break down ambiguous problems and apply analytical rigor
- ✓Strong judgment across data quality, model selection, and business impact tradeoffs
- ✓Collaborative mindset and experience working cross-functionally
