Senior Specialist, Data Scientist

BNY Mellon; Pittsburgh (Allegheny County); On-site; USD 90,000 - 120,000; Full time

Job summary

BNY Mellon is seeking a Data Scientist / Data Engineer to join its Payments Risk Services team in Pittsburgh, PA. You will learn payments data end-to-end and build ML models to detect and prevent fraudulent payments.

You'll work on feature pipelines, explainable AI, and dashboards to communicate impact to technical and business stakeholders. Ideal for a recent graduate with strong Python, ML, and data engineering foundations.

Qualifications

• 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, and hands-on coding in Python with libraries such as scikit-learn, pandas, and a deep-learning framework (PyTorch/TensorFlow).
• Familiarity with SQL and working with large datasets; exposure to distributed/cloud data tools (e.g., Spark, cloud data platforms) is a plus.
• Curiosity about fraud detection, anomaly detection, or risk analytics - a demonstrated approach to understanding a problem domain and translating it into data-driven solutions.
• Interest in or exposure to explainable AI (XAI) and responsible/ethical AI practices.
• Strong problem-solving and communication skills, with the ability to explain technical results to non-technical audiences.
• Internship, academic project, or coursework experience in ML, data engineering, or the financial services industry is a plus.

Responsibilities

• Learn the payments data landscape - Explore, profile, and understand transaction, customer, and channel data across payment rails to build the foundation for detection models.
• Build fraud-detection ML models - Develop, train, and validate supervised and unsupervised models that flag fraudulent payments in batch and near-real-time.
• Develop a fraud typology-driven approach - Understand major categories of payments fraud and map to detection signals and modeling strategies.
• Engineer features and data pipelines - Design and maintain feature pipelines that feed models, partnering with data engineering to move from prototype to production.
• Leverage AI to strengthen detection - Identify opportunities to apply modern AI techniques to improve fraud coverage and reduce false positives.
• Build explainable AI (XAI) - Apply model-interpretability methods so fraud analysts and regulators understand why a payment was flagged.
• Measure and communicate impact - Track model performance and present findings to technical and business stakeholders.
• Own projects from inception to delivery - Partner with fraud SMEs, product, and engineering to take detection ideas from hypothesis through deployment and monitoring.

Skills

Python

SQL

Pandas

scikit-learn

PyTorch/TensorFlow

Spark

Education

Bachelor's degree in STEM

Tools

Spark

Cloud data platforms

Job description

We're seeking a future team member to join our Payments Risk Services team as a Data Scientist / Data Engineer. In this role you'll learn our payments data end-to-end and build machine learning models that detect and prevent fraudulent payments. This role is located in Pittsburgh, PA, and is well-suited to a recent college graduate eager to apply data science to a high-impact, real-world problem.

In this role, you'll make an impact in the following ways:

• Learn the payments data landscape - Explore, profile, and understand transaction, customer, and channel data across payment rails (wire, ACH, RTP/instant) to build the foundation for detection models.
• Build fraud-detection ML models - Develop, train, and validate supervised and unsupervised models (classification, anomaly detection, graph/network analysis) that flag fraudulent payments in batch and near-real-time.
• Develop a fraud typology-driven approach - Understand the major categories of payments fraud - account takeover, authorized push payment (APP)/scams, synthetic identity, business email compromise, money mule/laundering patterns - and map each to detection signals and modeling strategies for how to detect and address them.
• Engineer features and data pipelines - Design and maintain reliable feature pipelines (behavioral, velocity, device, network, and aggregate features) that feed models, partnering with data engineering to move from prototype to production.
• Leverage AI to strengthen detection - Identify opportunities to apply modern AI techniques (e.g., deep learning, embeddings, LLMs for unstructured signals, foundation/graph models) to improve fraud coverage and reduce false positives.
• Build explainable AI (XAI) - Apply model-interpretability methods (SHAP, LIME, counterfactuals, reason codes) so fraud analysts, model risk, and regulators can understand why a payment was flagged.
• Measure and communicate impact - Track model performance (precision/recall, false-positive rate, fraud dollars prevented), and clearly present findings and recommendations to both technical and business stakeholders.
• Own projects from inception to delivery - Partner with fraud SMEs, product, and engineering to take detection ideas from hypothesis through deployment and monitoring.
• Stay current - Follow fraud trends, emerging attack patterns, and advances in ML/AI and responsible-AI practices relevant to the banking industry.
• Grow across the data science domains - Build depth in model science, feature science, and insight science, strengthening core skills in programming, math & statistics, distributed computing, and communicating complex results.
  

To be successful in this role, we're seeking the following:

• 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, and hands-on coding in Python with libraries such as scikit-learn, pandas, and a deep-learning framework (PyTorch/TensorFlow).
• Familiarity with SQL and working with large datasets; exposure to distributed/cloud data tools (e.g., Spark, cloud data platforms) is a plus.
• Curiosity about fraud detection, anomaly detection, or risk analytics - a demonstrated approach to understanding a problem domain and translating it into data-driven solutions.
• Interest in or exposure to explainable AI (XAI) and responsible/ethical AI practices.
• Strong problem-solving and communication skills, with the ability to explain technical results to non-technical audiences.
• Internship, academic project, or coursework experience in ML, data engineering, or the financial services industry is a plus.