Data Scientist Lead
Summary
Leads the bank's Data Science & AI efforts: coordinates a team of data scientists, oversees end-to-end ML delivery (data sourcing through deployment and monitoring), ensures model governance and CBN regulatory compliance, and pilots new AI capabilities such as GenAI, LLMs, RAG, and reinforcement learning, with a focus on cloud platforms like Azure.
The Senior Data Scientist will drive the design, development, adoption, and deployment of advanced analytics and AI solutions across all business units in the bank. The role will be responsible for collaborating with a team of data scientists in developing AI/ML models at scale, ensuring model governance and regulatory compliance, and supporting business units to embed AI into products, processes, and decision-making. The ideal candidate is both a strong technical leader and a strategic business partner.
- Support the development and execution of the bank’s Data Science & AI strategy in alignment with strategic business goals.
- Coordinate a team of data scientists and machine learning/AI developers in driving AI implementation across business and operations.
- Collaborate with senior business leaders to identify high-impact and emerging AI opportunities to gain a competitive edge.
- Oversee end-to-end delivery of machine learning models: data sourcing, feature engineering, model development, evaluation, deployment, and monitoring.
- Work closely with Data Engineering to ensure high-quality datasets for modeling.
- Ensure all models comply with internal model risk guidelines, CBN requirements, and global best practices.
- Stay updated with global AI trends, tools, and frameworks (GenAI, LLMs, RAG, agents, reinforcement learning).
- Lead Proof of Concept (PoC) and pilot initiatives for new AI capabilities.
- Bachelor’s degree in computer science, Statistics, Mathematics, Engineering, or related field..
- Master’s Degree and/or professional certifications (e.g., Azure ML, AWS ML, TensorFlow, Databricks, SAS, Agent orchestration, Microsoft Fabrics) can be an advantage.
Experience
- 6–10 years of experience in Data Science, Artificial Intelligence, or Machine Learning.
- Minimum 3 years in a senior technical role.
- Experience in banking or financial services is highly preferred.
- Proven track record of training and deploying ML and AI models to production at scale.
- Demonstrated experience with cloud platforms (particularly Microsoft Azure) and modern ML/AI tooling.
Behavioural Competencies
- Adopting Practical Approaches
- Articulating Information
- Challenging Ideas
- Developing Expertise
- Documenting Facts
- Embracing Change
- Interpreting Data
- Managing Tasks
Technical Competencies
- Big Data Frameworks and Tools
- Data Engineering
- Data Integrity
- IT Knowledge
- Stakeholder Management (IT)