About this role.
Signifyd is seeking a Chargebacks Data Analyst to investigate payment disputes, improve recovery performance, and reduce fraud-related financial losses for merchants. The role combines large-scale data analysis, chargeback-policy expertise, dashboard development, and operational process improvement. The analyst will partner with data science, BI, product, engineering, and merchant teams to identify root causes and translate findings into measurable actions. Key technical requirements include SQL, Python, Databricks, Looker, payment-processor knowledge, and familiarity with card-network dispute regulations. This Brazil-based remote role requires strong English communication, discretion with sensitive financial data, and the ability to manage changing priorities independently.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would first validate the underlying data and metric definitions, then segment results by processor, merchant, card network, reason code, geography, evidence type, and submission timing. I would compare the affected period with a baseline, identify statistically meaningful changes, review representative cases, and prioritize root causes with the largest financial impact. I would then recommend corrective actions and track whether the interventions improve recovery rates.
I would align with stakeholders on decisions the dashboard must support and define trusted metric logic before building. Core measures would include dispute volume, win rate, recovery amount, loss rate, representment turnaround time, reason-code mix, and performance by merchant, processor, and network. I would use SQL to create validated datasets, document definitions and refresh logic, and build Looker views that allow drill-down from executive trends to operational case detail.
Card-network rules define deadlines, eligible dispute rights, required evidence, reason-code handling, and escalation paths. Analytics should therefore measure operational compliance with these requirements, such as evidence completeness and response timeliness, while separating avoidable process failures from cases that are unlikely to be recoverable under network policy. This helps teams focus improvement efforts on controllable drivers.
I would use SQL to extract and aggregate transaction, dispute, and workflow data from governed sources, ensuring the joins and definitions are auditable. In Python, I could profile missing values, identify anomalous changes in reason-code or processor patterns, and create a repeatable model or reporting workflow. I would validate findings with domain stakeholders before operationalizing the output in a dashboard or alert.
I would begin with the business impact, such as increased loss exposure or reduced recovery, then present a concise explanation of the evidence and the primary driver. I would use clear visuals and avoid unnecessary technical detail while documenting assumptions and limitations. Finally, I would propose specific owners, actions, expected impact, and a measurement plan to confirm whether the solution works.
At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy.
Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here!
Position Overview
You’ll join our Chargeback Investigations team as a Data Analyst, serving as a front-line financial driver and data integrity gatekeeper for Signifyd. In this role, you will leverage data to uncover insights, optimize dispute strategies, and minimize financial losses while mitigating fraud and chargeback risks for our customers.
Using your skills in data analysis, trend identification, and chargeback research, you will help track recovery performance, ensure claims align with card network policies, and deliver actionable data to help merchants and internal teams optimize their workflows for success. This is a high-impact role offering tremendous opportunities for growth, learning, and cross-functional leadership.
What You’ll Do
- Work independently and with your team to help achieve the overall goals of the department.
- Gather feedback and complete research for new products, features and developments
- Enhance our operational workflows via process improvements and identification of automation opportunities.
- Oversee quality assurance checks on new features
- Support Product teams in different Product and feature launch stages, ensuring alignment on timelines, requirements and success criteria.
- Develop metrics and projections on large datasets to extract trends, measure results and outcomes, identify areas of opportunity, and predict future performance.
- Turn insights into compelling business cases and recommendations that drive action and support business decisions based on insights gained from your data analysis.
- Build analytical tools and dashboards to quantify the impact of Chargeback Investigations team initiatives and identify opportunities for improvement
- Develop a deep understanding of how existing metrics are calculated, and ensure metrics source data is reliable and measures our effectiveness at combating integrity issues
- Collaborate with data science, BI, and product teams, prioritize issue resolution, and establish and maintain an operating rhythm and accountability to identify and resolve.
- Partnering with engineering, product, and merchant teams to fix root-cause vulnerabilities that generate avoidable chargebacks.
Requirements
- Fluent in English (written and verbal).
- Minimum of 2 years of experience in data-driven analytical roles (e.g., financial analysis, product analytics, business intelligence, or risk/operations).
- Proven data analysis capabilities, with experience using quantitative data to identify chargeback trends, optimize dispute win rates, and drive operational decision-making.
- Knowledge and experience using SQL, Python, Databricks and Looker
- Hands-on experience with payment processors across LatAm, EMEA, or North America.
- In-depth knowledge of card network regulations (Visa, Mastercard, American Express, Discover) specifically surrounding chargeback cycles, dispute rights, and merchant/consumer guidelines.
- Strong analytical and problem-solving skills, with the ability to translate complex transaction datasets and dispute metrics into clear actionable insights.
- Highly professional and adaptable, capable of keeping pace with changing environments while maintaining strict adherence to Signifyd’s Security and Privacy policies.
- Strict discretion and confidentiality when handling sensitive consumer data, financial metrics, and payment details.
- Excellent time management and organizational skills, with a proven ability to prioritize heavy data workloads, multitask, and work flexible hours as needed.
- Collaborative team player with a proactive attitude, comfortable executing independent data investigations as well as collaborating cross-functionally to improve chargeback processes.
Highly Desired
- Experience using a variety of payment platforms or processors across (e.g. Shopify, Stripe, PayPal, Adyen, Braintree)
- Experience analyzing transaction logs, chargeback documentation, and complex datasets.
- Proven track record of directly improving chargeback win rates or reducing overall chargeback-to-transaction ratios.
- Prior experience using automated chargeback management platforms or merchant portals (e.g., Ethoca, Verifi/Order Insight, Chargebacks911, Kount).
- Familiarity with local and alternative payment methods in key markets (e.g., Pix in LatAm, iDEAL or Klarna in EMEA) and their specific dispute mechanisms.
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We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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