Data Scientist II - Digital Intelligence

Careers; New York (NY); On-site; USD 120,000 - 155,000; Full time

Job summary

Careers is partnering with a high-growth leader in digital identity, fraud prevention, and risk intelligence to join their Digital Intelligence team as a Data Scientist II. You will build features, models, and signals powering real-time fraud detection and identity decisions at scale.

You will work with huge telemetry datasets to uncover patterns, develop production-grade signals, and contribute to model explainability and dashboards across a sophisticated ML platform.

Qualifications

• 5+ years of experience in Data Science, ML, or analytics engineering.
• Strong Python and SQL skills with production ML experience.
• Experience building models, features, and data pipelines.

Responsibilities

• Build machine learning features, models, and analytical methods for fraud detection and identity verification.
• Analyze large-scale, high-cardinality datasets to identify predictive signals.
• Investigate fraud patterns including automation, spoofing, and device fingerprinting.
• Design and execute model validation strategies: holdout testing, drift detection, leakage reviews, stability assessments.
• Collaborate with Engineering, Product, Analytics, and Risk to productionize DS initiatives.
• Contribute to model explainability, documentation, dashboards, and production-readiness reviews.
• Communicate findings to both technical and non-technical stakeholders.

Skills

Python

SQL

Machine Learning

Feature Engineering

Model Evaluation

Distributed Computing

Tools

Pandas

NumPy

Scikit-learn

XGBoost

TensorFlow

PyTorch

Spark

PySpark

Databricks

Job description

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.