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.