Data Scientist ll - Digital Intelligence at Socure (San Francisco, CA)Data Scientist ll - 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 Data Scientist II to join our Digital Intelligence team. In this role, you will develop machine learning features, analytical methods, and production-oriented risk signals using device, network, browser, mobile, API, session, and behavioral telemetry. This is a hands-on role for a data scientist who can independently deliver well-scoped projects, work with complex and noisy data, and partner with engineering, product, and risk teams to improve fraud detection, identity confidence, and customer outcomes. You will deepen your expertise in Digital Intelligence while contributing to models and signals used in real-world production decisions. Job Responsibilities:

• Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.
• Work on scoped fraud and identity risk problems where data quality, labels, telemetry coverage, and product tradeoffs need careful analysis.
• Build features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.
• Analyze signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, and device or session fragmentation.
• Design and execute validation analyses, including train/test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.
• Use supervised, unsupervised, statistical, and heuristic approaches to identify durable fraud and identity risk signals.
• Investigate imperfect labels, delayed outcomes, instrumentation gaps, and changing fraud patterns to distinguish useful signal from data artifacts.
• Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.
• Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.
• Communicate methods, assumptions, findings, limitations, and recommendations clearly to technical and cross-functional stakeholders.
• Support junior data scientists and analysts through code review, analytical feedback, and sharing effective modeling and validation practices.

Job Requirements:

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field, or equivalent practical experience.
• 5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role.
• Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals.
• Strong SQL skills and experience working with large-scale, complex datasets.
• Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
• Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks.
• Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.
• Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions.
• Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact.
• Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use.
• Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders.
• Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions.

Preferred Qualifications:

• Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
• Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing.
• Experience developing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
• Familiarity with production ML workflows, model monitoring, feature monitoring, or batch and near-real-time decisioning systems.
• Experience with dashboarding, model explainability, feature documentation, or customer-impact analysis.
• Interest in adversarial behavior, fraud patterns, telemetry quality, and applied ML systems that operate in real-world production environments.