Analyst, Fraud Strategy

About Grubhub

At Grubhub, we champion restaurants from coast to coast. Restaurants sit at the heart of communities. It’s our mission to strengthen their roots, deepen their connections, and increase the positive impact they have on people and society.

Grubhub, part of Wonder, delivers the best local, authentic cuisine right to diners’ doors—and new customers and billions in revenue to local businesses. Featuring over 375,000 merchants in over 4,000 cities nationwide, our innovative technology, user-friendly platforms, and streamlined delivery capabilities have made us an industry leader in the world of online food ordering.

Since we opened our doors in 2004, Grubhub has been opening doors all across the country. Bakery doors in Hyde Park, jibarito joint doors in Queens, and doors of opportunity all across the country. Join our team and help us open more.

As a Fraud Analyst, you will play a critical role in protecting the business from fraud losses while enabling sustainable growth. You will leverage data, analytics, and cross-functional collaboration to design fraud prevention strategies, build solutions, and deliver actionable insights that drive impact.

• Assess fraud risks, evaluate controls, and identify gaps to help prioritize fraud mitigation projects.

• Develop and implement fraud detection rules using segmentation, predictive models, hypothesis testing, and other analytical methods.

• Investigate and analyze the latest fraud trends, designing proactive strategies to address them.

• Own and manage key processes to reduce fraud losses, including real‑time monitoring, alerting, and performance tracking.

• Partner with stakeholders across Finance, Technology, Product, Marketing, Logistics, Legal, PR, Security, and other teams to leverage both company and third‑party data.

• Design and execute A/B tests to measure the effectiveness of fraud prevention strategies.

• Conduct ad‑hoc analyses, clearly communicate results, and present actionable insights to both senior management and technical/non‑technical stakeholders.

• Independently manage small to medium‑sized projects, while supporting larger initiatives with cross‑functional collaboration.

• Education & Experience

  • Bachelor’s degree in a quantitative discipline (or equivalent work experience).

  • 3–5 years of experience in fraud, risk, payments, analytics, or statistical roles.

  • Prior exposure to statistics and machine learning modeling techniques through coursework or professional experience.

• Technical Skills

  • Proficiency in SQL and experience with R or Python for data analysis and statistical modeling.

  • Hands‑on experience with data visualization tools such as Tableau or Excel.

  • Experience automating reporting and building monitoring dashboards.

  • Knowledge of fraud vectors, tools, strategies, and infrastructure in high-volume digital environments.

• Core Competencies

  • Strong quantitative, analytical, and problem‑solving skills with sharp attention to detail. High level of inquisitiveness, initiative, and critical thinking.

  • Proven ability to translate analysis into implementable strategies and rules.

  • Strong project management skills, including cost‑benefit assessment, prioritization, documentation, and follow‑through.

  • Excellent communication skills, able to convey findings clearly to technical and non‑technical stakeholders.

• Experience in a digital marketplace or platform environment with high transaction volumes.

• Familiarity with advanced fraud detection systems, real‑time monitoring frameworks, or machine learning approaches.

• Strong track record of effectively presenting insights to senior management and executive stakeholders.

Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in‑person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.

Base Salary
: $86,500-$92,000 per year.

Wonder uses geographic‑specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take…