Staff Data Scientist (RiskOS)

Join Socure as a Staff Data Scientist for RiskOS, where you will lead the end-to-end development of data-driven solutions, collaborate with engineering and platform teams, and mentor other data scientists. This is a highly collaborative, hands-on technical leadership role focused on fraud and risk analytics, and Generative AI. Enjoy comprehensive benefits, including remote work, generous PTO, and a self-education allowance.

• End-to-end development of data-driven solutions on the RiskOS platform, including data exploration, modeling, and production deployment.
• Collaboration with engineering and platform teams to build scalable, production-grade pipelines and services, and with product and risk leaders to ensure actionable insights.
• Development and implementation of advanced analytics on RiskOS data to understand user behavior, product usage, fraud patterns, and workflow effectiveness.
• Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a related quantitative field, or equivalent professional experience
• Strong experience applying Generative AI in production or near‑production contexts, including:
• Familiarity with privacy‑preserving ML techniques, secure data handling, and regulatory requirements in fintech, credit, or public‑sector environments is strongly preferred
• Deep proficiency in Python and SQL, with hands‑on experience using ML frameworks such as scikit‑learn, XGBoost, TensorFlow, or PyTorch, plus modern GenAI/LLM tooling (e.g., OpenAI/Anthropic APIs, Hugging Face ecosystems, orchestration frameworks)
• Product‑minded and outcome‑oriented: you care about how models and GenAI tools are used, how they shape user experience and risk posture, and how to measure their real‑world impact
• Solid understanding of data engineering concepts, including ETL, data warehousing, schema design, and distributed computing
• Demonstrated experience building and maintaining scalable data pipelines and deploying ML models in production environments, ideally involving streaming or near‑real‑time data and modern data platforms (e.g., Databricks, Spark, PySpark, BigQuery, or similar)
• Building and evaluating LLM‑based applications or agents (e.g., retrieval‑augmented generation, workflow assistants, data‑insight copilots)
• Prompt design and optimization, safety and guardrail techniques, and quantitative/qualitative evaluation of LLM outputs
• Experience with platform‑oriented data science: working with feature stores, model‑serving infrastructure, CI/CD for ML, automated monitoring, and feedback collection workflows
• Proven ability to collaborate effectively in cross‑functional, fast‑paced teams; strong communication skills with comfort presenting trade‑offs and recommendations to senior stakeholders
• Hands‑on experience wrangling messy, high‑volume datasets: designing robust cleaning, normalization, and quality‑control processes; reasoning under missing or biased data; and building reusable data abstractions for other users
• 6+ years of hands‑on experience in data science, machine learning, or high‑scale data engineering roles, with a proven track record in fraud prevention, risk analytics, or complex decisioning systems
• Direct experience with fraud/risk modeling, identity verification, or trust & safety
• Prior work on orchestration platforms, case‑management tools, or rules/decision engines
• Experience mentoring senior ICs and setting technical direction for a small data science group