Data Scientist – Payments Risk Analytics

Role Overview

• Analyze end-to-end ACH payment decisioning performance, including ML risk scores and risk rules, across approvals and losses.
• Detect and analyze emerging trends in customer behavior, payment outcomes, and risk signals.
• Evaluate how changes in strategies, data, or rules impact downstream decisioning and outcomes over time.
• Independently define analytical questions, assemble datasets from multiple sources, and iterate toward insights without predefined reporting templates.
• Use Python to perform exploratory data analysis, feature evaluation, cohort analysis, and experimentation across large ACH and risk datasets.
• Develop repeatable analytical workflows and lightweight tooling in Python to accelerate insight generation and reduce manual analysis overhead.
• Perform ad hoc analyses to evaluate the value and usefulness of new or existing data signals for risk decisioning.
• Design analyses and reporting that support ongoing risk reviews, strategy discussions, and portfolio-level monitoring.
• Explore and apply established and emerging analysis techniques to accelerate insight generation, trend detection, and decision support within regulated risk and payment datasets.
• Partner with Risk, Product, and Relationship Management teams to inform strategy refinement and prioritization.
• Communicate trends, findings, and recommendations clearly to internal stakeholders and, when applicable, external clients.
• Work with large, imperfect, and regulated datasets to form actionable conclusions despite data gaps, latency, or attribution challenges.

Requirements

• 3+ years of experience in payments risk, fraud analytics, or decisioning performance analysis within fintech, payments, or e-commerce.
• Experience working with ACH or bank transfer payment data strongly preferred.
• Strong SQL skills and experience working with large transactional datasets.
• Strong Python skills for data analysis (e.g., pandas, notebooks), experimentation, and analytical automation.
• Experience managing and analyzing data related to risk-based decision systems, or similar decisioning frameworks.
• Ability to translate complex analytical findings into clear, actionable recommendations for technical and non-technical audiences.
• Comfort operating in ambiguous environments with incomplete, delayed, or imperfect data.
• Experience using AI-assisted tools or models for data analysis, pattern discovery, or insight generation is a plus.

Tech Stack

• Pandas
• Python
• SQL