As the Data Science Manager at Kuda, you will be responsible for leading a dynamic team of data scientists to develop and deploy machine learning models that drive critical business outcomes across the entire credit lifecycle. Your team will focus on building solutions for credit scoring, fraud detection, and collections, while also supporting various other business functions by providing data-driven insights and predictive capabilities.
You will work with cutting-edge technologies in data science and machine learning, with an emphasis on building scalable, real-time decisioning systems that are integrated into our product offerings. This means that your models will not only be developed but also put into production in a way that supports live, real-time decisions, enhancing Kuda’s ability to serve customers with speed and precision.
Your role will require collaboration across multiple cross-functional teams, including product, business, technology, and data teams, ensuring that all teams are aligned to deliver value through innovative, data-driven solutions. As a manager, you’ll foster an environment of continuous learning and improvement, ensuring that your team stays ahead of the curve in terms of industry trends, emerging technologies, and the most effective methodologies in the field of data science.
Key to your success will be your ability to translate business challenges into data-driven solutions while balancing technical execution with strategic vision. Your leadership will help scale Kuda’s impact, bringing high-quality, machine learning-based credit solutions to millions of customers across Nigeria and beyond.
Key Responsibilities
- Team Leadership: Manage and mentor a team of data scientists, fostering a collaborative and innovative environment.
- Model Development: Lead the design, development, and deployment of machine learning models for credit scoring, fraud detection, and collections.
- Cross-Functional Collaboration: Work closely with product, engineering, and compliance teams to integrate models into production systems.
- Data Analysis: Analyze large, complex datasets to extract actionable insights and inform business strategies.
- Model Monitoring: Oversee the performance of deployed models, ensuring they meet business objectives and regulatory standards.
- Stakeholder Communication: Present findings and recommendations to senior leadership and other stakeholders.
- Continuous Improvement: Stay abreast of industry trends and emerging technologies to continuously enhance model performance and team capabilities.
