Senior Data Scientist, Digital Identity & Fraud Signals

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United States

Hybrid

USD 180,000 - 320,000

Full time

14 days+

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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 telemetry into production-grade risk signals, guide evaluation methods, and influence

Qualifications

Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or related field.
12+ years of experience in data science, applied ML, statistics, or related roles.
Significant experience building, deploying, validating, and improving production ML models, risk signals, or decisioning systems.
Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or adversarial data domains.
Expert-level SQL skills and experience with large-scale, complex datasets.
Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark.
Deep understanding of supervised/unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring.
Ability to work with imperfect labels, delayed outcomes, telemetry gaps, and changing fraud patterns.
Strong judgment across data quality, modeling approach, feature design, explainability, and business impact.
Experience influencing data architecture, instrumentation, feature logging, and product direction through credibility.
Excellent communication and mentorship skills.

Responsibilities

Lead high-impact machine learning and feature development across device, network, browser, mobile, session, and behavioral intelligence.
Own ambiguous fraud and identity risk problems where data quality, labels, and adversarial behavior must be evaluated with product tradeoffs.
Develop production risk signals and models balancing detection, false positives, coverage, latency, explainability, robustness, and maintainability.
Build scalable feature-engineering for high-cardinality telemetry.
Investigate complex signal patterns such as spoofing, proxies, low-entropy fingerprints, telemetry gaps, and device fragmentation.
Define evaluation methods including holdout design, leakage checks, drift monitoring, and signal stability.
Influence telemetry collection and data contracts with engineering, product, risk, and platform teams.
Translate product and risk questions into data science approaches and production-ready roadmaps.
Raise standards for feature quality, validation, explainability, and governance.
Communicate tradeoffs and results clearly to stakeholders.
Mentor data scientists to improve framing, validation, and coding quality.