Socure - Data Scientist ll

• 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. • 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. • What You’ll Gain • What You’ll Gain • You will work on meaningful data science problems in fraud prevention and identity verification, using high-scale Digital Intelligence telemetry to build features and risk signals that contribute to real-world production decisions. • You will gain deeper experience with device, network, browser, mobile, session, and behavioral intelligence while working closely with senior data scientists, engineering, product, and risk partners. This role offers the opportunity to grow from independently delivering scoped modeling projects toward owning broader workstreams and developing Senior-level technical judgment over time.