Senior Data Scientist

BNY

. Explore, profile, and understand transaction, customer, and channel data across payment rails .

Posted 10/15/2026full-timePittsburgh • Pennsylvania • United StatesSenior💰 $71,000 - $123,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and validating fraud-detection models using machine learning techniques, with a strong foundation in statistics and programming. Proficient in applying explainable AI methods and collaborating with cross-functional teams to enhance fraud detection capabilities.

Highest-signal resume keywords

Machine LearningFraud DetectionPython ProgrammingExplainable AIData Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatisticsFraud Detection ModelsData AnalysisFeature EngineeringModel Performance TrackingAnomaly DetectionRisk AnalyticsDeep LearningSQL

Soft Skills

Problem-SolvingCommunication

Tools & Technologies

PythonScikit-learnPandasPyTorchTensorFlowSparkCloud Data Platforms

Industry Keywords

Payments FraudResponsible AIEmerging Attack PatternsFinancial Services

Tech Stack

Tools & technologies

CloudPandasPythonPyTorchRuby on RailsScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

Explore, profile, and understand transaction, customer, and channel data across payment rails
Develop, train, and validate supervised and unsupervised fraud-detection models
Understand payments-fraud categories and map them to detection signals and modeling strategies
Design and maintain behavioral, velocity, device, network, and aggregate feature pipelines
Partner with data engineering to move prototypes into production
Apply modern AI techniques to improve fraud coverage and reduce false positives
Apply explainable-AI methods including SHAP, LIME, counterfactuals, and reason codes
Track model performance, including precision/recall, false-positive rate, and fraud dollars prevented
Present findings and recommendations to technical and business stakeholders
Partner with fraud SMEs, product, and engineering from hypothesis through deployment and monitoring
Follow fraud trends, emerging attack patterns, ML/AI advances, and responsible-AI practices
Build expertise in model science, feature science, insight science, programming, mathematics, statistics, distributed computing, and communicating complex results

Requirements

What you’ll need

Bachelor's degree in a STEM field (Computer Science, Data Science, Statistics, Mathematics, Engineering, or related), or equivalent experience
Foundational knowledge of machine learning and statistics
Hands-on coding in Python with scikit-learn, pandas, and a deep-learning framework (PyTorch/TensorFlow)
Familiarity with SQL and working with large datasets
Curiosity about fraud detection, anomaly detection, or risk analytics
Interest in or exposure to explainable AI (XAI) and responsible/ethical AI practices
Strong problem-solving and communication skills
Internship, academic project, or coursework experience in ML, data engineering, or the financial services industry is a plus
Exposure to distributed/cloud data tools such as Spark or cloud data platforms is a plus

Benefits

Comp & perks

Highly competitive compensation
Flexible global resources and tools for life's journey
Wellbeing programs
Paid leaves, including paid volunteer time
401(k) plan
Company-sponsored medical insurance
Company-sponsored dental insurance
Company-sponsored vision insurance
Basic life insurance for the employee and eligible dependents
Vacation time
Sick time
Annual discretionary incentive award eligibility