Data Scientist

We are seeking a Data Scientist to join our Global Technology Division. This role sits within a highly strategic engineering team focused on predictive enterprise optimization and internal mobility platforms. You will hold the keys to a core mathematical engine, designing and deploying sophisticated recommendation models and matching indexes that mathematically balance complex business constraints against corporate operational overhead.

• Algorithm & Index Design: Develop, tune, and maintain semantic matching algorithms, recommendation engines, or Natural Language Processing (NLP) models to map unstructured text profiles against highly technical corporate frameworks.
• Predictive Optimisation Modeling: Build mathematical optimization models evaluating personnel distribution variables alongside geographic constraints and operational cost parameters to calculate cost-effective resource strategies.
• Upholding Statistical Truth: Champion mathematical and statistical rigor. Ensure all machine learning models accurately handle data imbalances, control for historical performance biases, and rigorously evaluate algorithmic fairness.
• Collaborative AI Deployment: Work closely with upstream data teams to track model metrics, monitor algorithmic prediction drift, and safely surface confidence scores to executive decision-makers.

• Experience: Intermediate experience as a Data Scientist, Machine Learning Engineer, or Quantitative Analyst within an enterprise environment (Fintech, Banking, or Scale-up SaaS preferred).
• Python Mastery: Complete fluency in Python and specialized machine learning/statistical libraries (scikit-learn, Sci Py, stats models). Hands‑on exposure to NLP frameworks or text embeddings (spaCy, Hugging Face) is highly valued.
• Statistical Rigor: A solid foundation in applied statistics, including clustering, regression architectures, and predictive modeling validation techniques.
• Exploratory Data Storytelling: Ability to visually explain algorithm performance trends (using Plotly, Seaborn, etc.) and present model logic transparently to senior management.