Data Scientist ll - Digital Intelligence

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.

• 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.

• 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, Num Py, scikit‑learn, XGBoost, Tensor Flow, 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.

• Background in fraud…