Job Description

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.