Data Scientist

Market insights

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Description

Key Responsibilities Analyze large operational datasets to identify patterns, anomalies, outliers, and risk indicators. Develop approaches for anomaly detection, clustering, segmentation, and risk scoring. Build supervised machine-learning models where historical outcomes or labelled cases are available. Perform feature engineering using structured and unstructured data. Design evaluation approaches to assess model performance, including precision, recall, false positives, and false negatives. Work with domain experts to determine how analytical outputs should be incorporated into operational workflows. Build and maintain data-processing and feature pipelines to support modelling at scale. Develop production-ready model or scoring pipelines and integrate them with applications built by the forward-deployed teams. Monitor model performance, data quality, and data drift after deployment. Conduct exploratory analysis to identify new opportunities for workflow redesign and automation. Support the use of LLMs and other AI techniques alongside traditional statistical and machine-learning approaches where appropriate. Develop reusable analytical approaches and components that can be applied across multiple agency engagements. Requirements & Qualifications A. Educational Qualification & Experience Education: Degree in Data Science, Computer Science, Statistics, Machine Learning, Data analytics or information systems is preferred. Experience: Experience working with large, messy opera

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