Data Scientist II - Digital Intelligence

We're partnering with a high-growth leader in digital identity, fraud prevention, and risk intelligence that is transforming how organizations establish trust online. They are looking for a Data Scientist II to join their Digital Intelligence team and help build the machine learning models, features, and risk signals that power real-time fraud detection and identity decisions at scale.

In this role, you'll work with massive volumes of device, network, browser, mobile, session, and behavioral telemetry to uncover patterns, develop production-grade signals, and improve fraud prevention outcomes across a sophisticated ML platform.

What You'll Be Doing

* Build machine learning features, models, and analytical methods focused on fraud detection, identity verification, and risk intelligence.

* Analyze large-scale, high-cardinality, sparse, and noisy datasets to identify meaningful patterns and predictive signals.

* Investigate sophisticated fraud behaviors including automation, spoofing, emulators, VPN/proxy usage, low-entropy fingerprints, and telemetry anomalies.

* Design and execute model validation strategies including holdout testing, drift detection, leakage reviews, stability assessments, and customer impact analysis.

* Partner closely with Engineering, Product, Analytics, and Risk teams to move data science initiatives into production.

* Contribute to model explainability, feature documentation, dashboards, and production-readiness reviews.

* Communicate findings and recommendations to both technical and non-technical stakeholders.

What We're Looking For

* 5+ years of experience in Data Science, Machine Learning, Statistical Modeling, Analytics Engineering, or a related field.

* Strong Python skills with experience using libraries such as Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.

* Advanced SQL skills and experience working with large, complex datasets.

* Experience developing machine learning models, predictive features, and analytical pipelines.

* Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, and statistical analysis.

* Experience with distributed data processing tools such as Spark, PySpark, or Databricks.

* Ability to work independently while collaborating across cross-functional teams.

Preferred Experience

* Fraud detection, cybersecurity, identity verification, trust & safety, anomaly detection, or risk modeling.

* Device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, or telemetry processing.

* Production ML systems, model monitoring, and real-time or near real-time decisioning environments.

* Experience working with adversarial datasets and evolving fraud patterns.

Why Join?

* Work on highly impactful, real-world machine learning challenges.

* Help build systems that prevent fraud and improve digital trust at scale.

* Collaborate with experienced data scientists, engineers, and product leaders.

* Gain deep expertise in digital intelligence, behavioral analytics, and identity risk modeling.

* Opportunity to grow into a senior-level technical contributor while working on production ML systems used by leading organizations.

Interested in learning more? Reach out directly for a confidential conversation.