Data Scientist

Key Responsibilities:  • Analyse 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 delivery 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 project engagements. Requirements & Qualifications • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline. • 3–5 years of relevant experience in data science, machine learning, advanced analytics, or a similar role. • Experience working with large, messy operational or transactional datasets. • Experience in applied data science, including statistics, feature engineering, machine learning and model evaluation. • Experience with techniques such as anomaly detection, classification, clustering or risk modelling. • Experience building data pipelines and taking models beyond experimentation into production environments. B. Technical & Professional Competencies Essential (Must-Have) • Strong proficiency in Python and SQL. • Able to translate a business or operational problem into an analytical problem and determine the appropriate modelling approach. • Familiarity with software engineering practices including Git, automated testing and APIs. • Comfortable assessing model trade-offs and explaining them clearly to non-technical stakeholders. Preferred (Advantageous) • Familiarity with containers, CI/CD or other DevOps / ModelOps practices would be advantageous. C. Soft Skills & Communication Attributes • Able to work independently in an ambiguous, fast-moving environment and collaborate closely with engineers, product teams and domain experts. • Strong sense of ownership for turning analysis into something that is used in an operational workflow.