Socure
Data Scientist
Overview
As a Data Scientist for Workforce Verification on the RiskOS team, you will own the end-to-end data science lifecycle for a critical new product area focused on workforce identity and hiring fraud.
About Socure
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts.
Requirements & Eligibility
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
- 3–6 years of hands-on experience in data science, machine learning, or applied analytics, with meaningful work on fraud, risk, trust & safety, or workforce/hiring analytics preferred.
- Experience owning end-to-end analytics and/or model development projects: problem framing, data wrangling, feature engineering, model training, evaluation, and deployment support.
- Strong proficiency in Python and SQL, including experience with common data science and ML libraries (e.g., pandas, scikit-learn, XGBoost, PySpark, or similar).
- Comfort working with large, messy, and heterogeneous datasets.
- Exposure to Natural Language Processing and/or unstructured text analytics.
Key Responsibilities
- Own the full data science lifecycle for Workforce Verification use cases on RiskOS — from data exploration and hypothesis generation through model development, evaluation, deployment, and monitoring.
- Explore and analyze workforce-related data sources to identify patterns of workforce fraud such as fake resumes, identity rental, deepfake interviews, and injection attacks.
- Design, implement, and iterate on rules, conditions, and heuristic logic in RiskOS workflows to detect high-risk workforce events.
- Develop and evaluate machine learning models for workforce risk and identity assessment.
- Collaborate with the RiskOS and Workforce product teams on GenAI-powered features such as the Resume Verification Agent and explanation agents.
- Partner closely with engineering to productionize models, rulesets, and GenAI components within RiskOS.
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