Data Scientist (Digital Intelligence)

Join our Digital Intelligence team as a Data Scientist II. In this hands-on role, you will develop machine learning features, analytical methods, and production-oriented risk signals using various telemetry data. You will work independently on well-scoped projects, analyze complex data, and collaborate with engineering, product, and risk teams to improve fraud detection and customer outcomes. This position offers a comprehensive benefits package, including health coverage, parental leave, 401k, and remote work options.

• 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.
• Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.
• Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks
• Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions
• Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis
• Strong SQL skills and experience working with large-scale, complex datasets
• Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions
• 5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role
• Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders
• Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use
• 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
• Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar
• Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact
• Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals
• Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing
• Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain
• 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