Fraud Data Analyst

About Us

Riskified empowers businesses to unleash ecommerce growth by taking risk off the table. Many of the world's biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists and researchers, Riskified's AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights. Riskified is proud to work with incredible companies in virtually all industries including Booking.com, Acer, Gucci, Lorna Jane, GoPro, and many more.

We thrive in a collaborative work setting, alongside great people, to build and enhance products that matter. Abundant opportunities to create and contribute provide us with a sense of purpose that extends beyond ourselves, leaving a lasting impact. These sentiments capture why we choose Riskified every day.

About the Role

As a fraud analytics expert, you will collaborate closely with internal teams such as Product and Data Science. Your main responsibilities will include preventing large-scale fraud events and researching fraud behavior patterns to optimize model performance. You will apply your operational fraud experience to develop and implement features and tools directly into our production decision engine.

What You'll Be Doing
Become an expert in Card-Not-Present fraud prevention through hands-on analysis of live data on Riskified's fraud detection platforms.
Utilize your analytical experience to identify and prevent emerging fraud patterns in production using our ML detection tools.
Generate actionable insights for implementation in production, enabling the decision engine to identify and decline fraudulent orders for our merchants.
Develop leads for integration into Riskified's fraud detection tools.
Automate analytical processes for fraud detection, documentation, and data visualization.
Deliver in-depth analytical findings and employ advanced technical approaches for presentations to both technical and non-technical stakeholders.
Qualifications
2+ years of experience in the fraud domain
2+ years of hands-on experience with SQL, R, or Python
Strong analytical skills with the ability to interpret complex data
Proven ability to manage multiple projects and tasks concurrently
Bachelor's degree, preferably in a highly-quantitative field (e.g., mathematics, statistics, computer science, natural sciences), economics, or business administration
Fluent in written and verbal English
Hands-on experience with Looker or Databricks - Preferred
Life at Riskified

We are a fast-growing and dynamic tech company with 750+ team members globally. We value collaboration and innovative thinking. We're looking for bright, driven, and passionate people to grow with us.

Our Tel-Aviv team is currently working in a hybrid of remote and in-office work. We have recently moved to our new space in Tel Aviv - check it out here!

Some of our Tel Aviv Benefits & Perks:

Equity for all employees, Keren Hishtalmut, pension
Private medical insurance, extra time off for parents and caregivers
Commuter and parking benefits
Team events, fully-stocked kitchen,lunch stipend, happy hours, yoga, pilates, functional training, basketball, soccer
Wide-ranging opportunities to volunteer and make an impact
Commitment to your professional development with global onboarding, skills-based courses, full access to Udemy, lunch & learns
Awesome Riskified gifts and swag!
In the News

Geektime: Riskified Goes Public

Walla!: Happy Hour at the Riskified Offices

Geektime Insider: A look at Riskified Tel Aviv

Globes: Riskified to contribute the highest amount up to date to Tmura

Globes: Riskified is among Israel's fastest growing companies

TechCrunch: Riskified Prevents Fraud on Your Favorite E-commerce Site

Riskified is deeply committed to the principle of equal opportunity for all individuals. We do not discriminate based on race, color, religion, sex, sexual orientation, national origin, age, disability, veteran status, or any other status protected by law.