Staff Data Scientist - Digital Intelligence

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. Job Summary: Socure is the leading provider of digital identity verification and fraud prevention solutions, using AI and machine learning to power accurate identity trust decisions.

Our mission is to eliminate identity fraud and ensure online trust across industries. We are seeking a Staff Data Scientist to join our Digital Intelligence team. In this role, you will provide technical leadership for turning noisy, high-scale device, network, browser, mobile, API, and behavioral telemetry into production-grade fraud and identity risk signals.

This is a hands-on technical leadership role. You will lead ambiguous signal-development efforts, define rigorous evaluation methods, influence what telemetry we collect, and help set the technical direction for how Digital Intelligence detects risky behavior, recognizes trustworthy devices and sessions, and adapts to adversarial change.

Job Responsibilities: Lead high-impact machine learning and feature-development initiatives across device, network, browser, mobile, session, and behavioral intelligence. Own ambiguous fraud and identity risk problems where data quality, label reliability, adversarial behavior, customer impact, and product tradeoffs must be evaluated together.

Develop production risk signals and models that balance fraud detection, false-positive risk, coverage, latency, explainability, robustness, and operational maintainability. Build and guide scalable feature-engineering approaches for high-cardinality, sparse, noisy, and platform-dependent telemetry.

Investigate complex signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, device fragmentation, and over-linkage risk. Define evaluation methods for Digital Intelligence signals, including holdout design, leakage checks, drift monitoring, adversarial robustness, customer impact analysis, and long-term signal stability.

Influence telemetry collection, data contracts, feature logging, model monitoring, and production readiness in partnership with engineering, product, risk, and platform teams. Translate open-ended product, customer, and fraud-risk questions into clear data science approaches, measurable hypotheses, and production-ready signal roadmaps.

Raise team standards for feature quality, model validation, explainability, documentation, and risk-signal governance. Communicate technical recommendations, tradeoffs, limitations, and results clearly to data science peers, engineering partners, product stakeholders, risk teams, and senior leadership.

Mentor data scientists by improving problem framing, modeling judgment, validation rigor, code quality, and ability to operate independently in ambiguous domains. Job Requirements: Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field. 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.

Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems. Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.

Expert-level SQL skills and extensive experience working with large-scale, complex, noisy datasets. Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools. Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation.

Demonstrated ability to work with imperfect labels, delayed outcomes, telemetry artifacts, instrumentation gaps, and changing fraud patterns. Strong judgment across data quality, modeling approach, feature design, explainability, operational complexity, and business impact.

Experience influencing data architecture, instrumentation, feature logging, and product direction through technical credibility rather than direct authority. Excellent communication skills, including the ability to explain complex data science decisions and risk tradeoffs to technical and non-technical audiences.

Strong mentorship skills and a track record of improving the technical quality and judgment of other data scientists