Data Scientist II
at Socure
Hub - San Francisco; Hybrid - Seattle, WA; New York - Remote; Hybrid - Miami, FL, hybrid, mid, full time, USD 1,40,000–1,70,000 / year, 5+ years
posted 27 Jul 2026
- scikit-learn
- PySpark
- Feature Engineering
- Unsupervised Learning
- Machine Learning
- NumPy
- SQL
- pandas
- Supervised Learning
- PyTorch
- XGBoost
- Statistical Modeling
- Spark
- Databricks
- Python
- TensorFlow
About the role
Data Scientist ll - Digital Intelligence
Remote: yes Posted: 2026-07-27
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.
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.
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