data
Posted Mar 26Staff Data Scientist - RiskOS
at Socure
United StatesHybrid
Responsibilities
- - Architect and build scalable data pipelines and production ML workflows, collaborating with data engineering to ensure robust, reliable, and efficient data processing for both batch and streaming use cases.
- - Lead the design, execution, and analysis of experimentation frameworks to optimize user journeys, feature adoption, and workflow performance across the RiskOS platform.
- - Define rigorous evaluation frameworks for GenAI solutions, including offline benchmarks, human‑in‑the‑loop review, safety and hallucination checks, and impact measurement in production.
- - Own end‑to‑end deployment of production‑grade solutions: packaging models and GenAI workflows, integrating with RiskOS services, establishing SLAs, and instrumenting telemetry, alerting, and feedback loops.
- - Develop and automate tools for model evaluation, stress testing, backtesting, and adversarial scenario simulation to ensure robustness and operational resilience—especially in high‑risk fraud and compliance contexts.
- - Collaborate cross‑functionally with product, engineering, risk, solution consulting, and customer‑facing teams to translate business
Requirements
- ABOUT THE ROLE Socure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate decisions.
- As a Staff Data Scientist for RiskOS, you will sit at the intersection of platform data science, fraud and risk analytics, and Generative AI.
- experience with Generative AI to design, evaluate, and operationalize LLM‑powered tools that improve analytics, workflows, and case investigations.
- - Lead the creation and evaluation of Generative AI solutions (LLMs, agents, prompt‑based tools) that automate analytics, power case review and investigation assistants, streamline documentation, and enhance RiskOS workflows and reporting.
- - Partner with platform and engineering teams to define and build core RiskOS data science infrastructure, including feature stores, model‑serving APIs, evaluation services, and monitoring frameworks for both traditional ML and GenAI systems.
- 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. - Strong
- experience applying Generative AI in production or near‑production contexts, including: - 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. - 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). - 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). - Solid understanding of data engineering concepts, including ETL, data warehousing, schema design, and distributed computing. -
- Experience with platform‑oriented data science: working with feature stores, model‑serving infrastructure, CI/CD for ML, automated monitoring, and feedback collection workflows. - Hands‑on
- requirements in fintech, credit, or public‑sector environments is strongly preferred. - Proven ability to collaborate effectively in cross‑functional, fast‑paced teams; strong communication skills with comfort presenting trade‑offs and recommendations to senior stakeholders. - Product‑minded and outcome‑oriented: you care about how models and GenAI tools are used, how they shape user
- 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.
Contact
- Follow Us! YouTube https://www.youtube.com/c/Socure | LinkedIn https://www.linkedin.com/company/socure/ | X (Twitter) https://x.com/socureme | Facebook https://www.facebook.com/socure/
Additional details
- WHY 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.
- The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
- We hire people who want that level of responsibility.
- People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision.
- If you want predictability or narrow scope, this won’t be your place.
- If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
- Our mission is to eliminate identity fraud and ensure online trust across industries.
- You will own end‑to‑end development of data‑driven solutions on the RiskOS platform—from heavy‑duty data exploration and cleaning, through modeling and GenAI agent design, all the way to production deployment and monitoring.
- You will leverage your expertise in fraud and risk management to help develop and integrate robust detection and decisioning models, and your
- You will collaborate closely with engineering and platform teams to build scalable, production‑grade pipelines and services, and with product and risk leaders to ensure RiskOS delivers actionable insights, self‑serve analytics, and best‑in‑class fraud prevention at scale.