Job Description:

The Opportunity

The Chief Technology Office (CTO) is responsible for shaping and delivering the technology strategy, architecture, platforms, and infrastructure that support the global operations. The function plays a central role in driving technology transformation, operational resilience, and continuous improvement across the bank's technology landscape.

As part of the CTO organisation, the Data team works closely with technology, operations, and business stakeholders to understand current processes, identify opportunities for improvement, skills gaps, and support the successful delivery of strategic workforce initiatives. The team provides critical analysis and

reporting capabilities that help ensure the global workforce are aligned to business objectives and delivered effectively.

This is an excellent opportunity to join a high-profile function and contribute to initiatives that enhance the efficiency, scalability, and effectiveness of the global workforce.

The Opportunity

Core Focus: Algorithm Design, Machine Learning/NLP, Statistical Rigor

About the Role

Once our 6 core datasets are unified, you will hold the keys to the mathematical engine. We are seeking an algorithmic Data Scientist to design and deploy a proprietary Skills Matching Index.

Your goal is to build the recommendation models that match at-risk or unallocated employees with live vacancies and long-term skills gaps across the global bank. By factoring in building costs and geographic parameters, your algorithm will mathematically optimize where talent is deployed to minimize operational overhead while preserving top performers.

Key Responsibilities

• Algorithm C Index Design: Develop and tune semantic matching algorithms, recommendation engines, or Natural Language Processing (NLP) models to map employee skills profiles against text-heavy job descriptions and vacancies.
• Workforce Optimization Modeling: Build predictive optimization models that evaluate employee skills alongside geographic data and building costs to calculate the most cost-effective location strategy.
• Upholding Statistical Truth: Champion mathematical rigor across the project. Ensure models

accurately handle data imbalances, control for historical bias in performance reviews, and avoid false positives in skills matching.

• Collaborative AI Deployment: Work alongside the Data Analyst to feedback model metrics, track algorithmic prediction drift, and safely surface model confidence scores to executive decision-makers.

Required Technical Skills

• Experience: 2–4 years of experience as a Data Scientist, Machine Learning Engineer, or

Quantitative Analyst. Exposure to recommendation algorithms, NLP, or People Analytics is a major plus.

• Python Mastery: Fluent in Python and specialized machine learning libraries (scikit-learn, SciPy, statsmodels). Experience with NLP frameworks (spaCy, HuggingFace, or text embeddings) is highly valued.
• Statistical Rigor: Solid foundation in applied statistics, including clustering, predictive modeling, regression, and algorithmic fairness evaluations.
• Exploratory Data Storytelling: Ability to visually explain algorithm performance (using Plotly, Seaborn, etc.) and present model logic transparently to senior management.