Senior Data Scientist

Jobtailor; Pittsburgh (Allegheny County); On-site; USD 110,000 - 160,000; Full time

Job summary

Jobtailor is seeking a data scientist focused on fraud detection to join our Pittsburgh team. You will build and validate ML models, profile payments data, and design feature pipelines across rail systems to detect fraud in near-real-time.

The ideal candidate blends solid ML fundamentals with strong communication, collaborating with fraud SMEs, product, and engineering to deliver explainable AI and responsible analytics in a fast-paced financial services setting.

Qualifications

• Bachelor's degree in a STEM field 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 (classification, anomaly detection, graph/network analysis) that flag fraudulent payments in batch and near-real-time.
• Develop a fraud typology-driven approach — Understand major categories of payments fraud and map each 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 stakeholders can 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 ideas from hypothesis through deployment and monitoring.
• Stay current — Follow fraud trends and advances in ML/AI for banking.
• Grow across data science domains — Build depth in model science, feature science, and insight science, strengthening core skills.

Skills

Machine Learning Model Development

Python Programming

Fraud Detection Techniques

Explainable AI

Data Pipeline Engineering

Education

Bachelor's degree in STEM field

Tools

Scikit-learn

Pandas

PyTorch

TensorFlow

Spark

Cloud Data Platforms

Job description

• 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.

Requirements

• 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.

Core Competencies

Demonstrates expertise in building and validating fraud-detection machine learning models, leveraging advanced AI techniques, and engineering reliable data pipelines. Proficient in communicating complex results to diverse stakeholders while maintaining a strong foundation in statistics and programming.

Highest-signal resume keywords

• Machine Learning Model Development
• Python Programming
• Fraud Detection Techniques
• Explainable AI (XAI)
• Data Pipeline Engineering

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Statistics
• Data Engineering
• Anomaly Detection
• Feature Engineering
• Graph Analysis
• Deep Learning
• SQL
• Python
• Data Analysis

Soft Skills

• Problem-Solving
• Communication
• Curiosity

Industry Keywords

• Fraud Detection
• Risk Analytics
• Financial Services
• Responsible AI
• Data Science

Tools & Technologies

• Scikit-learn
• Pandas
• PyTorch
• TensorFlow
• Spark
• Cloud Data Platforms