About the job
Join Ravelin, the forefront of fraud detection technology! We leverage cutting-edge machine learning and network analysis to tackle significant challenges in online transaction security. Our mission is to ensure that online transactions are safe, allowing our clients to confidently serve their customers.
We believe in fostering a vibrant workplace culture characterized by empathy, ambition, unity, and integrity. At Ravelin, we emphasize work-life balance and maintain a flat hierarchy across the company. By becoming part of our team, you will quickly gain insights into the latest technologies while collaborating with some of the brightest minds in the industry. Check out our Glassdoor reviews!
If you resonate with our values and enthusiasm for preventing fraud, we encourage you to explore our blog to see how you can contribute to safeguarding the world's largest online businesses.
The Role
We are excited to welcome a Junior Data Scientist to our dynamic team of client-facing data scientists and support analysts. In this pivotal role, you will delve into client data to uncover patterns and trends related to fraud. By applying exploratory analysis, you will create meaningful narratives from data, probing not just “what” and “how” but also “why.” Your interactions with clients will be crucial in helping them understand their fraud landscape and refining our focus on targeted solutions.
Responsibilities
- Engage directly with clients to deliver insights from fraud analytics and machine learning models.
- Examine large datasets to pinpoint fraud patterns, trends, and emerging threats, enhancing client outcomes.
- Collaborate with clients to address specific fraud challenges, developing effective and innovative solutions.
- Compile reports and present analytical findings to clients as needed.
- Identify new model features and enhance model efficacy.
- Optimize graph network performance through network analysis.
- Gain practical experience with our cloud infrastructure and utilize available tools to enhance client data and performance.
- Develop internal tools in Python to improve our analytical abilities.

