Data Scientist (Payments Risk Analytics)

Nuvei

8h•Remote

About The Position

The world of payment processing is rapidly evolving, and businesses are looking for loyal and strategic partners, to help them grow. Meet Nuvei, Nuvei is the global fintech building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, banking, risk, and fraud management services. Connecting businesses to their customers in more than 200 markets, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights for customers and partners to succeed locally and globally through one integration. At Nuvei, we live our core values, and we thrive on solving complex problems. We’re dedicated to continually improving our product and providing relentless customer service. We are always looking for exceptional talent to join us on the journey! We are looking for a Risk & Payments Intelligence Analyst to join our Risk & Fraud team. In this role, you will analyze data related to ACH transactions, ML risk scores, risk rules, decisioning systems, and vendor data to understand performance, surface emerging trends, and inform ongoing optimization of payment program decisions. This role focuses on portfolio and system level analysis rather than case-by-case transaction review or manual queues, and requires strong analytical judgment in ambiguous, data-constrained environments, including the evaluation of new data, analytical methods, and AI-assisted approaches as our risk capabilities evolve.

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.

Nice To Haves

Experience using AI-assisted tools or models for data analysis, pattern discovery, or insight generation is a plus.

Responsibilities

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.