M09 - Data Scientist

Role Overview We are seeking a Data Scientist to support multiple product squads working with government agencies to redesign operational workflows and develop AI-enabled solutions. The successful candidate will be responsible for analysing large operational datasets, identifying patterns and anomalies, and developing machine learning models to support risk assessment, decision-making, and workflow automation. This is a hands-on role that involves end-to-end data science activities, including exploratory data analysis, feature engineering, model development, data pipeline implementation, and production deployment. Key Responsibilities • Analyse large and complex operational datasets to identify patterns, anomalies, outliers, and potential risk indicators. • Develop and implement machine learning techniques, including anomaly detection, clustering, segmentation, classification, and risk scoring. • Build and optimise supervised machine learning models using historical data and labelled datasets. • Perform data preprocessing, cleaning, transformation, and feature engineering using structured and unstructured data. • Design and implement model evaluation frameworks, including precision, recall, false positives, false negatives, and other relevant performance metrics. • Collaborate with business stakeholders and domain experts to translate operational challenges into data-driven solutions. • Design, develop, and maintain scalable data processing pipelines and feature engineering workflows. • Develop production-ready machine learning models and scoring pipelines, integrating them with applications developed by engineering teams. • Monitor deployed models to ensure performance, data quality, reliability, and detection of data drift. • Conduct exploratory data analysis to identify opportunities for process improvement, workflow redesign, and automation. • Explore and apply Large Language Models (LLMs) and other AI techniques alongside traditional statistical and machine learning approaches where appropriate. • Develop reusable analytical frameworks, models, and components to support multiple projects and government agency engagements. • Work closely with engineers, product teams, and stakeholders to ensure successful deployment and adoption of data science solutions. Requirements • Bachelor's Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, Information Systems, or a related discipline preferred. • Proven experience in applied data science, statistical analysis, machine learning, and predictive modelling. • Strong proficiency in Python and SQL for data analysis, manipulation, and model development. • Hands-on experience with machine learning techniques, including anomaly detection, classification, clustering, and risk modelling. • Experience working with large, complex, and unstructured operational or transactional datasets. • Strong understanding of data preprocessing, feature engineering, statistical modelling, and model evaluation techniques. • Experience developing data pipelines and deploying machine learning models into production environments. • Ability to translate business and operational requirements into analytical problems and appropriate modelling approaches. • Familiarity with software engineering practices, including Git, automated testing, and API integrations. • Strong understanding of model performance metrics, trade-offs, and validation methodologies. • Ability to communicate technical findings, model outcomes, and recommendations to non-technical stakeholders. • Strong analytical, problem-solving, and critical-thinking skills. • Ability to work independently and manage multiple projects in a fast-paced environment. • Strong collaboration skills and a proactive approach to delivering practical, production-ready solutions. Preferred Qualifications • Experience with containerisation, CI/CD pipelines, DevOps, or MLOps practices. • Familiarity with Large Language Models (LLMs), Generative AI, and AI-enabled applications. • Experience integrating machine learning models into enterprise applications and operational workflows. • Prior experience supporting government agencies or public sector digital transformation initiatives will be advantageous.