Socure is building the identity trust infrastructure for the digital economy â 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 wonât be your place. If you want to help build the future of identity with a team that holds a high bar for itself â keep reading.
Socure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
As a Staff Data Scientist for RiskOS, you will sit at the intersection of platform data science, fraud and risk analytics, and Generative AI. You will own endâtoâend development of dataâdriven solutions on the RiskOS platformâfrom heavyâduty 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 experience with Generative AI to design, evaluate, and operationalize LLMâpowered tools that improve analytics, workflows, and case investigations.
You will collaborate closely with engineering and platform teams to build scalable, productionâgrade pipelines and services, and with product and risk leaders to ensure RiskOS delivers actionable insights, selfâserve analytics, and bestâinâclass fraud prevention at scale.
This is a highly collaborative, handsâon technical leadership role for someone who enjoys owning complex data problems endâtoâend and acting as a force multiplier for other data scientists and product teams.
Develop and implement advanced analytics on top of noisy, heterogeneous RiskOS data to understand user behavior, product usage, fraud patterns, and workflow effectiveness; translate findings into concrete product and risk strategy improvements.
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.
Lead the creation and evaluation of Generative AI solutions (LLMs, agents, promptâbased tools) that automate analytics, power case review and investigation assistants, streamline documentation, and enhance RiskOS workflows and reporting.
Define rigorous evaluation frameworks for GenAI solutions, including offline benchmarks, humanâinâtheâloop review, safety and hallucination checks, and impact measurement in production.
Partner with platform and engineering teams to define and build core RiskOS data science infrastructure, including feature stores, modelâserving APIs, evaluation services, and monitoring frameworks for both traditional ML and GenAI systems.
Own endâtoâend deployment of productionâgrade 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 resilienceâespecially in highârisk fraud and compliance contexts.
Enable product and risk teams through selfâserve analytics and tools: build dashboards, template analyses, and GenAIâdriven assistants that help nonâtechnical users explore RiskOS data, tune workflows, and debug decisions.
Collaborate crossâfunctionally with product, engineering, risk, solution consulting, and customerâfacing teams to translate business requirements into dataâdriven solutions and actionable insights, particularly for fraud and risk use cases on RiskOS.
Mentor and provide technical guidance to other data scientists and analysts, modeling best practices in experimentation, software engineering hygiene, GenAI safety, and rigorous model evaluation.
Ensure all solutions adhere to best practices in data privacy, security, and compliance, especially when handling sensitive PII and financial data in regulated fintech and publicâsector environments.
Contribute to companyâwide standards for ML and GenAI explainability, risk evaluation, feature logging, and documentation, helping raise the overall AI bar across Socure.
Communicate complex technical concepts and findings clearly to both technical and nonâtechnical stakeholders, including executive leadership and external partners.
Masterâs or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a related quantitative field, or equivalent professional experience.
6+ years of handsâon experience in data science, machine learning, or highâscale 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 nearâproduction contexts, including:
Building and evaluating LLMâbased applications or agents (e.g., retrievalâaugmented generation, workflow assistants, dataâinsight copilots).
Prompt design and optimization, safety and guardrail techniques, and quantitative/qualitative evaluation of LLM outputs.
Deep proficiency in Python and SQL, with handsâon experience using ML frameworks such as scikitâlearn, 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 nearârealâtime 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 platformâoriented data science: working with feature stores, modelâserving infrastructure, CI/CD for ML, automated monitoring, and feedback collection workflows.
Handsâon experience wrangling messy, highâvolume datasets: designing robust cleaning, normalization, and qualityâcontrol processes; reasoning under missing or biased data; and building reusable data abstractions for other users.
Familiarity with privacyâpreserving ML techniques, secure data handling, and regulatory requirements in fintech, credit, or publicâsector environments is strongly preferred.
Proven ability to collaborate effectively in crossâfunctional, fastâpaced teams; strong communication skills with comfort presenting tradeâoffs and recommendations to senior stakeholders.
Productâminded and outcomeâoriented: you care about how models and GenAI tools are used, how they shape user experience and risk posture, and how to measure their realâworld impact.
Direct experience with fraud/risk modeling, identity verification, or trust & safety.
Prior work on orchestration platforms, caseâmanagement tools, or rules/decision engines.
Experience mentoring senior ICs and setting technical direction for a small data science group.
Please note that we are unable to provide sponsorship for this role; now or in the future.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, nation
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