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Data Scientist
The Opportunity
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.
Key Responsibilities
- 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.
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Required Technical Skills
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- 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, SciPy, statsmodels). Hands-on exposure to NLP frameworks or text embeddings (spaCy, HuggingFace) 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.
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