Role Overview
The Data Scientist II position at Socure involves developing machine learning features, analytical methods, and production-oriented risk signals utilizing device, network, browser, mobile, API, session, and behavioral telemetry. This remote role, based out of the San Francisco hub, suits an experienced data scientist with a background in applied machine learning who can independently deliver well-scoped projects and work with complex data.
Key Responsibilities
Create machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.
Address fraud and identity risk problems requiring analysis of data quality, labels, telemetry coverage, and product tradeoffs.
Construct features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.
Examine signal patterns including spoofing, emulator behavior, automation, proxy and VPN usage, low-entropy fingerprints, and session fragmentation.
Execute validation analyses such as train and test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.
Apply supervised, unsupervised, statistical, and heuristic approaches to detect durable fraud and identity risk signals.
Address imperfect labels, delayed outcomes, instrumentation gaps, and shifting fraud patterns.
Collaborate with senior data scientists, engineering, product, risk, and platform teams to refine requirements, prepare data, and support production rollouts.
Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.
Share technical methods, assumptions, findings, limitations, and recommendations with cross-functional stakeholders.
Guide junior data scientists and analysts through code reviews, analytical feedback, and effective modeling practices.
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.
Minimum of 5 years of professional experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical field.
Proven background in building, evaluating, and refining machine learning models, features, analytical pipelines, or risk signals.
Solid understanding of supervised and unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.
Capacity to navigate noisy data, imperfect labels, missing values, instrumentation gaps, and fluctuating data distributions.
Strong analytical judgment concerning data quality, feature design, model selection, explainability, and business impact.
Experience working alongside engineering, product, analytics, or risk teams to transition data science work into production.
Clear communication skills 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 tasks.
Nice to Have
Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or adversarial data domains.
Familiarity with device intelligence, browser and mobile fingerprinting, behavioral biometrics, network intelligence, VPN and proxy detection, or telemetry signal processing.
Experience generating features from high-cardinality categorical data utilizing techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
Exposure to 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.
Enthusiasm for adversarial behavior, fraud patterns, telemetry quality, and applied ML systems operating in production.
Skills and Tools
Python
PyTorch
TensorFlow
SQL
Spark
PySpark
Databricks
Pandas
NumPy
Scikit-learn
XGBoost
Salary and Benefits
Annual Salary Range: $140,000 to $170,000 USD
Equal opportunity employer supporting diversity across all demographics
Accommodation support available during the application and hiring process
About the Company
Socure builds the identity trust infrastructure for the digital economy, verifying identities in real time and stopping fraud before it starts. The organization provides digital identity verification and fraud prevention solutions powered by artificial intelligence and machine learning to ensure online trust across industries.
How to Apply
Interested candidates should apply directly on the official Socure careers page.
