Jobs at Kuda Bank

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

Requirements

• Bachelor's or Master's degree in a quantitative field (e.g. Computer Science, Mathematics, Statistics, Data Science, Economics)
• 3+ years of working experience as a software engineer (postgraduate experience included)
• Excellent problem-solving and analytical skills
• Strong communication skills to effectively communicate software design and progress to stakeholders
• Ability to work independently and as part of a team in a fast-paced environment
• Experience in the fintech industry
• Familiarity with Google Cloud Platform (GCP)

go to method of application »

As a Credit Strategy Analyst, you are supporting the analytics throughout the credit product life cycle, from product design, to credit acquisition, portfolio management, collection and profit & loss. You will work within the Decision Science team, reporting to the Credit Risk Analytics Lead.

Role and Responsibilities

• Portfolio performance metrics
• Policy rule analysis
• Profit and loss forecast
• Credit limit assignment analysis
• Affordability analysis
• Score cut-off analysis
• Collection rate
• Lending / Collection strategy A/B testing

Requirements

• Must have an academic background in a mathematical discipline e.g. Mathematics, Statistics, Physics, etc.
• Must have 3+ years experience as a quantitative analyst in a financial service
• Proficient at database languages SQL
• Proven knowledge of typical statistical tests used for A/B testing
• Proven knowledge of standard statistical software packages (open source language preferred)
• Understanding of credit portfolio performance metrics, including but not limited to annualised loss, vintage curves, roll rate, exposure at default, loss given default
• General programming language (open source language preferred, e.g. Python, R)
• Experience of developing business reports from end-to-end, i.e. from business requirement analysis, designing the visualisation layer of the reports, sourcing the data, developing and productionize the report, and managing the stakeholders’ expectations.
• Experience in generating actionable insights from the reports, e.g. interpreting the monthly monitoring reports to the business owners in relation to their business interest, and recommending actions in response to the latest developments in the reports