Fraud Data Analyst

McAfee

1d•Hybrid

About The Position

We are seeking an experienced Data Analyst to join our dynamic Payments & Risk team. In this role, you will be responsible for defining data requirements and building analytics dashboards to enable the risk team to detect, prevent, and mitigate fraud risks. You will analyze complex data sets, identify trends, and collaborate with cross-functional teams to ensure the security and integrity of our processes and to secure our brand reputation. You will work closely with payments, risk, marketing, data, and engineering teams to ensure the right data is available when it’s needed. This role requires ability to write queries and output data in an easily digestible format. The role also requires comfort managing the natural ebbs and flows of work, including responding to time sensitive requests that may occasionally arise outside of standard business hours. This is a hybrid role based in within a commutable distance of one of our hubs i.e. Frisco, TX or New York, NY or San Jose, CA or Newport Beach, CA. You will be required to be onsite on an as-needed basis; when not working onsite, you will work remotely from your home location.

Requirements

Minimum of 4+ years of experience in fraud analysis, or related data-centered roles, with a focus on digital goods, ideally software.
Experience with fraud detection vendors (e.g. Signifyd, Bolster, Arkose, others).
Familiarity with fraud detection systems, payment risk tools, or chargeback analysis.
Proficiency in data analysis and tools such as SQL, Databricks/Mosaic, PowerBI.
Advanced Excel knowledge (pivot tables, query functions, visualizations, etc.).
Ability to present complex information clearly, resulting in influencing senior leaders.
Consistent follow up, execution abilities are key.
Proven ability to work collaboratively in a fast-paced, cross-functional environment.

Nice To Haves

Experience with AI tools for building dashboards and analyzing complex datasets is a plus.
Experience working in subscription-based or digital commerce environments.
Experience analyzing affiliate marketing fraud, payment fraud, or account abuse.
Knowledge of machine learning techniques for fraud detection is a plus.

Responsibilities

Define data sources and data elements required to effectively understand our fraud vectors.
Have a clear understanding of each vendor with whom we work, their function, and the data they make available to us.
Write and optimize complex SQL queries to extract, transform, and analyze data from multiple databases.
Work with data engineers to improve data pipelines and ensure accurate fraud monitoring datasets.
Maintain data integrity and ensure proper documentation of fraud-related datasets.
Develop and maintain fraud risk measures and dashboards.
Follow the full data journey as it relates to the payment/fraud/business operations environment, including: Identifying issue(s) and potential areas of improvement, Determining the correct data sets to evaluate, Querying and validating the data, Working with other departments to enhance the data with otherwise missing information, Converting the data into a usable, human-readable form, Extrapolating root causes and recommended solutions, Identifying ROI, cost/benefit, and creating a business case for the solutions, Preparing the information and presenting it to leadership.
Ensure appropriate Key Risk Indicators are developed and maintained; identify and clearly articulate gaps.
Collaborate with Payments team and Business teams to incorporate emerging threats into our acquisition and account management strategies, reviewing partner and marketing agreements to identify vulnerabilities.
Monitor, analyze, and investigate transactions, customer accounts, and financial activities to identify potential fraudulent patterns or anomalies.