Manager: Data Analytics & AI (Internal Audit)

We’re offering an exciting opportunity to join GoTyme Bank’s Internal Audit team at the heart of a fast-growing, digital-first organisation. This role sits in a uniquely privileged position, providing exposure across our products, platforms, and systems—giving you a true end-to-end view of how a modern bank operates.

Given this breadth, the role offers significant opportunity for growth and impact. You’ll play a key role in shaping how we approach risk, efficiency, and innovation, contributing to a control environment that supports both regulatory compliance and business agility.

We’re looking for individuals with a multifaceted skill set and a curiosity that goes beyond traditional audit. A strong eye for detail and advanced data analytics capabilities are essential, while experience or interest in AI, automation, and rapid prototyping will set you apart. This role is ideal for someone who enjoys combining analytical thinking with hands-on technical problem-solving in a dynamic, innovation-driven environment.

Requirements

Experience and Skills Required:

• Bachelor's degree in Computer Science, Statistics, Economics, Business or related field. Postgraduate degree preferred.
• Proven experience (3 years) in data analysis, preferably in a business intelligence or analytics role.
• Proficiency in Python and SQL for data manipulation and analysis. Experience with data visualization tools such as Tableau or similar is a plus.
• Rapid prototyping.
• Workflow automation tools, n8n etc.
• Experience with prompt engineering.
• AI assisted scripting.
• Strong business acumen with the ability to understand and translate business needs into data-driven solutions.
• Solid understanding of statistical concepts and experience with predictive modeling techniques.
• Excellent communication and presentation skills with the ability to effectively convey complex ideas to diverse audiences.

Responsibilities:

• Data Analysis: Utilize Python and SQL to extract, clean, and analyze large datasets from various sources to derive actionable insights.
• Business Insight Generation: Translate data findings into meaningful business insights and recommendations, identifying trends, patterns, and opportunities for improvement.
• Reporting and Visualization: Develop dashboards and reports to present insights in a clear and concise manner to stakeholders at all levels of the organization.
• Collaboration: Work closely with cross-functional teams to understand business needs and deliver insights that support strategic assurance.
• Predictive Modeling: Apply statistical and machine learning techniques to build predictive models that enhance business forecasting and assurance.
• Continuous Improvement: Stay updated with the latest trends and advancements in data analytics, continuously seeking opportunities to improve processes and methodologies.
• Communication: Effectively communicate complex technical concepts and findings to non-technical stakeholders, fostering a data-driven culture within the organization.