Data Scientist ll

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.