Job Description: Qualification: Master's degree (16 years of education) in Data Science, Statistics, Computer Science, IT, Applied Mathematics or a related field from an HEC-recognized university. Preference would be given to foreign qualified candidates with proven track record. Experience: At least 7 years of applied data-science experience, with a track record of models taken from analysis to production use. Skills & Competencies: • Statistical modelling and machine learning (scikit-learn, XGBoost, etc.) • Python/R, SQL • Experiment design and model evaluation • Data visualization and communication of technical results to non-technical audiences Key Performance Indicators (KPIs): • Number of models developed with demonstrated predictive/decision value. Model accuracy and adoption by pillar squads and departments. Job Responsibilities: Key Responsibilities / ToRs: • Design and build predictive, diagnostic and optimization models (e.g., yield prediction, risk scoring, demand forecasting) for priority pillar use-cases. • Run exploratory analysis and feature engineering on departmental datasets in partnership with Data Engineers and Data Analysts. • Design experiments and evaluation frameworks to validate model performance, fair-ness and robustness before deployment. • Translate modelling results into clear recommendations and visualizations for pillar squads and departmental stakeholders. • Contribute to the Unit's applied-innovation agenda: proofs-of-concept and horizon scanning of new modelling techniques. Last date to apply: 12-Nov-2026