Staff Data Scientist - RiskOS

Socure Inc.

Job Overview

Location

Remote - US

Job Type

Full-time

Full Job Description

📋 Description

• As a Staff Data Scientist for RiskOS at Socure, you will play a pivotal role in advancing the company’s mission to build identity trust infrastructure for the digital economy by developing and deploying cutting-edge data science and Generative AI solutions that power real-time fraud prevention and identity verification at scale.
• You will own end-to-end development of sophisticated analytics, machine learning models, and LLM-powered agents on the RiskOS platform, translating complex data insights into actionable risk strategies while mentoring peers and shaping technical standards across the organization.
• Day to day, you will develop and implement advanced analytics on noisy, heterogeneous RiskOS data to uncover user behavior, fraud patterns, and workflow effectiveness, turning findings into concrete product and risk strategy improvements.
• You will architect and build scalable data pipelines and production ML workflows in collaboration with data engineering, ensuring robust processing for both batch and streaming use cases using modern platforms like Databricks, Spark, or BigQuery.
• You will lead the design, execution, and analysis of experimentation frameworks to optimize user journeys, feature adoption, and workflow performance across RiskOS, using rigorous statistical methods to measure impact.
• You will lead the creation and evaluation of Generative AI solutions—including LLMs, agents, and prompt-based tools—that automate analytics, power case investigation assistants, streamline documentation, and enhance reporting, while defining evaluation frameworks for safety, hallucination detection, and production impact.
• You will partner with platform and engineering teams to build core RiskOS data science infrastructure, such as feature stores, model-serving APIs, evaluation services, and monitoring frameworks for both traditional ML and GenAI systems.
• You will own end-to-end deployment of production-grade solutions, including packaging models and GenAI workflows, integrating with RiskOS services, establishing SLAs, and instrumenting telemetry, alerting, and feedback loops for operational resilience.
• You will develop and automate tools for model evaluation, stress testing, backtesting, and adversarial scenario simulation to ensure robustness in high-risk fraud and compliance contexts.
• You will enable product and risk teams through self-serve analytics by building dashboards, template analyses, and GenAI-driven assistants that empower non-technical users to explore data, tune workflows, and debug decisions.
• You will 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.
• You will ensure all solutions adhere to data privacy, security, and compliance best practices, especially when handling sensitive PII and financial data in regulated fintech and public-sector environments.
• You will contribute to company-wide standards for ML and GenAI explainability, risk evaluation, feature logging, and documentation, helping raise the overall AI bar across Socure.
• You will communicate complex technical concepts and findings clearly to both technical and non-technical stakeholders, including executive leadership and external partners, translating business needs into data-driven solutions.

🎯 Requirements

• Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a related quantitative field, or equivalent professional experience.
• 6+ years of hands-on experience in data science, machine learning, or high-scale data engineering, 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, 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 (scikit-learn, XGBoost, TensorFlow, PyTorch) and modern GenAI/LLM tooling (OpenAI/Anthropic APIs, Hugging Face, orchestration frameworks).
• Demonstrated experience building and maintaining scalable data pipelines and deploying ML models in production, ideally involving streaming or near-real-time data on platforms like Databricks, Spark, PySpark, or BigQuery.
• Solid understanding of data engineering concepts including ETL, data warehousing, schema design, and distributed computing, with hands-on experience wrangling messy, high-volume datasets and building reusable data abstractions.

🏖️ Benefits

• Opportunity to work on high-impact identity trust infrastructure used by businesses, governments, and millions of people daily to prevent fraud and verify identities in real time.
• Fully remote work arrangement within the United States, offering flexibility and autonomy in how and where you work.
• Collaborative, owner-minded culture that values critical thinking, technical excellence, and solving complex problems with precision and purpose.
• Access to cutting-edge AI and ML tools, including GenAI/LLM platforms and modern data platforms, to innovate at the forefront of fraud and risk technology.
• Clear path for technical leadership and influence, including mentoring senior ICs and shaping company-wide standards for ML, GenAI, and data science excellence.
• Commitment to diversity, equity, and inclusion, with Socure being an equal opportunity employer that does not discriminate based on protected characteristics and provides accommodations upon request.

Skills & Technologies

Python

TensorFlow

PyTorch

Apache Spark

Data Science

Senior

Remote

Degree Required

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Socure Inc.

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About Socure Inc.

Socure Inc. provides digital identity verification and fraud prevention software for financial services, fintech, e-commerce and government clients. The platform applies machine learning and graph-based analytics to link and validate identity elements in real time, detecting synthetic identities, account takeover and document fraud. It integrates via APIs and SDKs for onboarding, KYC/AML compliance and transaction monitoring, aiming to reduce false positives and manual reviews while improving approval rates for legitimate users worldwide.

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