Senior Specialist, Data Scientist

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

Job summary

BNY is seeking a data scientist/data engineer in Pittsburgh to learn the payments data landscape, build ML models to detect fraud, and deploy end-to-end solutions. The role targets a recent college graduate eager to apply data science to real-world issues in payments risk.

You will work on feature engineering, real-time/near-real-time detection, and explainable AI, while collaborating with fraud SMEs and engineering teams to move models from prototype to production.

Qualifications

• Bachelor's degree or equivalent in STEM.
• Foundational ML knowledge and Python coding experience.
• Familiarity with SQL and distributed/cloud data tools is a plus.

Responsibilities

• Learn payments data landscape from transaction, customer, and channel data.
• Develop, train, and validate supervised and unsupervised fraud models.
• Map fraud categories to detection signals and modeling strategies.
• Design and maintain feature pipelines with data engineering support.
• Apply AI techniques (deep learning, embeddings, LLMs) to improve detection.
• Use model interpretability methods to explain flags to stakeholders.
• Track and report model performance and impact to business.
• Take ideas from hypothesis to deployment and monitoring.
• Stay updated on fraud trends and responsible AI practices.
• Grow depth in model science, feature science, and insight science.

Skills

Python

ML fundamentals

Data analysis

Communication skills

Fraud detection interest

Education

Bachelor's degree in STEM or related

Tools

scikit-learn

PyTorch

TensorFlow

pandas

Spark

Job description

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.

Responsibilities

• 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.

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.

About Us

At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting‑edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.

Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what is all about. Join us and be part of something extraordinary.

About The Team

At BNY, our culture speaks for itself, check out the latest BNY news at BNY Newsroom & BNY LinkedIn.

Here’s a Few Of Our Recent Awards

• America’s Most Innovative Companies, Fortune, 2025
• World’s Most Admired Companies, Fortune 2025
• “Most Just Companies”, Just Capital and CNBC, 2025

Our Benefits And Rewards

BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay-for-performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter.

BNY is an Equal Employment Opportunity/Affirmative Action Employer - Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.

BNY assesses market data to ensure a competitive compensation package for our employees. The expected base salary for this position when employment commences can be found in the Job Info section at the bottom of the posting.

Base salary offered may vary depending on multiple individualized factors, including market location, job‑related knowledge, skills, and experience. Base salary is only part of the total rewards package, which may include eligibility for an annual discretionary incentive award. Subject to the terms and conditions of the applicable plans then in effect, eligible employees may enroll in a 401(k) plan as well as participate in Company‑sponsored medical, dental, vision, and basic life insurance plans for the employee and the employee’s eligible dependents. Eligible employees also may receive other benefits (including various paid time off benefits, such as vacation and sick time), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

If hired, the employee will be in an “at will” position and the Company reserves the right to modify base salary (as well as any other discretionary payments or compensation programs) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.