The Data Science team at Signifyd develops machine learning models that drive their fraud detection engine and enhances merchant payments experiences.
They focus on researching and experimenting to introduce new products while maintaining a hands-on approach throughout the development lifecycle.
The role involves collaborating with various teams, building production-level models, and optimizing algorithms for fraud detection.
This position emphasizes teamwork, curiosity, and a commitment to continuous improvement in combating fraud.Required Qualifications and Skills Required qualifications include a bachelor's degree in computer science, applied mathematics, economics, or an analytical field, with an advanced degree being a plus.
The position demands at least three years of hands-on experience in statistical analysis, experiment design, and coding, preferably in Python and Java.
Familiarity with SQL and Linux is also necessary, along with effective communication skills in English.
Candidates are expected to have a solid understanding of data collection and analysis in a collaborative environment.Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies.
The full external job listing link should always be relied on for authoritative information.Signifyd specializes in e-commerce fraud protection through the development and deployment of machine learning models that enhance decision-making processes around transaction validity and account security.
The company's product is designed to help businesses of various sizes reduce their fraud exposure while simultaneously improving the online shopping experience for consumers by making fraudulent activities less rewarding for perpetrators.
The Data Science and Engineering teams at Signifyd are adapted to remote collaboration, highlighting the company's commitment to flexibility and the importance of a strong remote work culture in tackling the challenges of fraud protection at scale.#J-18808-Ljbffr