Data Science Developer

Kuda is a money app for Africans on a mission to make financial services accessible, affordable and rewarding for every African on the planet.
We’re a tribe of passionate and diverse people who dreamed of building an inclusive money app that Africans would love so it’s only right that we ended up with the name ‘Kuda’ which means ‘love’ in Shona, a language spoken in the southern part of Africa.
We’re giving Africans around the world a better alternative to traditional finance by delivering money transfers, smart budgeting and instant access to credit through digital devices.
We’ve raised over $90 million from some of the world's most respected institutional investors, and we’re rolling out our game-changing services globally from our offices in Nigeria, South Africa, and the UK.

Role Overview

Reporting to the Director of Decision Science, the Data Science Developer will play a key role in bridging the gap between data science models and production deployment. This individual will assist in operationalizing machine learning models within the production environment and will be responsible for developing and implementing business rules within a commercial decision engine (a low-code software development environment). This role requires close collaboration with both the data team and the software engineering team.

Roles and responsibilities

• Deploying machine learning models developed by the Decision Science team into the production environment.
• Developing, testing, and implementing business rules within a commercial decision engine to automate decision-making processes.
• Collaborating with data engineers to ensure seamless data flow and integration for model deployment and rule execution.
• Working closely with software engineers to integrate deployed models and business rules into existing systems and applications.
• Participating in the monitoring and maintenance of deployed models and business rules, identifying and resolving issues as they arise.
• Contributing to the documentation of deployment processes, business rules, and system configurations.
• Examples of machine learning models and business rules to be deployed or developed:
• Credit approval scorecard
• Credit underwriting rules
• Fraud scorecard