Data Scientist II - Workforce Verification & Fraud Risk

Socure; New York (NY); On-site; USD 140,000 - 170,000; Full time

Job summary

Socure’s RiskOS team is building workforce verification capabilities to reduce hiring fraud and strengthen identity trust in real-world workflows. This onsite role in New York, NY, focuses on leading the end-to-end data science lifecycle for workforce identity and hiring risk use cases, leveraging multi-source data to uncover fraud signals.

You will collaborate across product and engineering to deploy rules, ML models, and GenAI components, with a strong emphasis on evaluation, monitoring, and

Qualifications

• Bachelor’s or Master’s degree in a quantitative field or equivalent practical experience.
• 3–6 years of hands-on data science, machine learning, or analytics in fraud/risk contexts.
• Experience owning end‑to‑end analytics and model development including problem framing, data wrangling, feature engineering, model training, evaluation, and deployment support.
• Strong Python and SQL skills with common data science libraries.
• Experience working with large, messy datasets and unstructured text analytics is a plus.

Responsibilities

• Own the full data science lifecycle for workforce verification and risk use cases.
• Explore diverse data sources to identify fraud signals and patterns.
• Design and iterate rules, heuristics, and machine learning components.
• Develop and evaluate models for fraud risk scoring and anomaly detection.
• Collaborate on GenAI-powered features and evaluation frameworks for LLM components.
• Work with engineering to productionize models and monitoring.

Skills

Fraud risk

Data science lifecycle

Model evaluation

Problem framing

Feature engineering

LLM evaluation

Education

Bachelor's or Master's degree in quantitative field

Tools

Python

SQL

pandas

scikit-learn

XGBoost

PySpark

Natural Language Processing

Generative AI

LLM APIs

RAG-style retrieval

Airflow

Spark

Job description

Socure’s RiskOS team is building workforce verification capabilities to reduce hiring fraud and strengthen identity trust in real-world workflows. This onsite role in New York, NY, focuses on leading the end-to-end data science lifecycle for workforce identity and hiring risk use cases, leveraging multi-source data to uncover fraud signals.

You will collaborate across product and engineering to deploy rules, ML models, and GenAI components, with a strong emphasis on evaluation, monitoring, and