Principal Associate, Data Scientist - Customer Protection Debit Transaction Fraud Data Science

Discover Financial
United States
Workplace: OnsiteFull timeUSD 161,800 - 184,600 annuallyFunction: Data Science & Machine LearningExperience: 5+ yearsSkills: ["Cross-functional collaboration","Translating complexity into business goals","Customer-first mindset","Problem solving","Model validation"]

Build machine learning models for debit authorization fraud prevention within the Bank Customer Protection Debit & Claims Data Science team. Partner with data scientists, analysts, engineers, and product managers to deliver decisioning systems that keep customers safe in real time. Use Python, AWS, Spark, SQL, and advanced ML (gradient boosting, representation learning, graph/sequence learning) across the full model lifecycle, from design through deployment and ongoing performance validation.

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Discover Financial
Discover Financial
1 week ago

Principal Associate, Data Scientist - Customer Protection Debit Transaction Fraud Data Science

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Source: Company careers pageValidated by: Fursa AI
Last checked: 2 hours agoStatus: Live

Job Summary

Build machine learning models for debit authorization fraud prevention within the Bank Customer Protection Debit & Claims Data Science team. Partner with data scientists, analysts, engineers, and product managers to deliver decisioning systems that keep customers safe in real time. Use Python, AWS, Spark, SQL, and advanced ML (gradient boosting, representation learning, graph/sequence learning) across the full model lifecycle, from design through deployment and ongoing performance validation.
Location: United States
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Partner with cross-functional teams to deliver products that measurably reduce debit fraud for customers.
  • •Use Python, Conda, AWS, H2O, Spark, and SQL to derive insights from large volumes of numeric and textual data.
  • •Build and run machine learning models across design, training, evaluation, validation, and implementation phases.
  • •Apply techniques from proven methods (e.g., gradient boosting) to frontier approaches (graph and sequence learning) for real-time fraud detection.
  • •Translate model complexity into tangible business goals and contribute to performance measurement and validation.

Pay and Benefits

Salary: USD 161,800 - 184,600 annually

Key Requirements

  • •Bachelors in a quantitative field plus 5 years of data analytics experience, or Masters (or MBA quantitative concentration) plus 3 years of experience, or a PhD in a quantitative field.
  • •At least 3 years of experience with machine learning for predictive tasks, especially classification on large, highly imbalanced datasets (fraud/risk/anomaly detection).
  • •At least 3 years of experience in Python and SQL; preferred production-quality Python and large-scale data processing with Spark.
  • •Experience building and tuning gradient boosting models (e.g., XGBoost, LightGBM, H2O) and deploying into real-time/production decision systems.
  • •Experience with automated modeling pipelines (e.g., Kubeflow Pipelines on Kubernetes or comparable MLOps tooling).
Experience:5+ yearsFintechFraud detectionRisk analyticsMachine learningData science
Education:
Skills:Cross-functional collaborationTranslating complexity into business goalsCustomer-first mindsetProblem solvingModel validation
Tech Stack:PythonCondaAWSH2OSparkSQLXGBoostLightGBMPolarsSnowflake/SnowparkPytestMypyLintingCI/pre-commitKubeflow PipelinesKubernetesGraph learningSequence learning

Eligibility

Work Authorization:Sponsorship available.

Company Brief

Discover Financial
Provides consumer banking products, credit cards, personal loans, and payment services through the Discover brand. It operates a major U.S. financial network and serves individuals and merchants with lending and digital payment solutions.
Industry: Retail Banking
Company Size: Enterprise (1,001+ employees)
Revenue: USD 10M to 25M
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Riverwoods, United States
Founded: 1985
WebsiteLinkedIn