Kyriba empowers CFOs, Treasurers, and their IT counterparts to transform treasury, payments, working capital, and connectivity solutions to activate liquidity as a dynamic, real-time vehicle for growth and value creation. Kyriba is a secure, scalable SaaS platform that leverages artificial intelligence, automates payments workflows, and enables thousands of multinational corporations and banks to maximize growth, protect against loss from fraud and financial risk, and reduce operational costs. With 2,000 clients worldwide, including 25% of Fortune 500 and Eurostoxx 50 companies, Kyriba manages more than 1.3 billion bank transactions per year, and 250 million payments for a total value of $15 Trillion annually. Kyriba is headquartered in San Diego, with offices in Dubai, Frankfurt, London, Paris, Shanghai, Singapore, Tokyo, Warsaw and other major locations. For more information, visit www.kyriba.com.

Role: 

The primary objective of this internship or gap year placement is to effectively employ anomaly detection algorithms for the continuous observation and management of Kyriba's production environment. This role offers a unique opportunity to delve into complex problem-solving scenarios, utilizing advanced algorithmic solutions to monitor and maintain the optimal function of the platform.

The intern/apprentice will not only be tasked with identifying anomalies, but will also engage in causal inference techniques to pinpoint the root cause of any incidents that occur. This intricate process requires a keen analytical mind and a deep understanding of the system's operation.

A strong foundation in probability theory is imperative for this role. The intern is expected to apply this knowledge in creating and implementing anomaly detection algorithms, as well as in conducting rigorous causal inference.

Furthermore, the intern will have the opportunity to utilize large language models (LLMs) for the analysis of application logs. LLMs are effective tools for extracting significant insights from large volumes of data, and the intern will be able to apply these models to identify anomalies, establish correlations, and aid in the development of mitigation strategies. This addition to the role emphasizes the value of machine learning and artificial intelligence in modern problem-solving and system analysis.

https://github.com/whylabs/whylogs

Profile:

Student engineering school or master's degree in data science, statistics, mathematics or applied mathematics

Machine learning

Probability

Curiosity

Solid knowledge of Python

Effective communication skills in English

Ability to work in a team