Data Scientist

Data Scientist (with Software Engineering Competency)GovTech Data Practice, MirageThe Privacy team within GovTech’s Data Engineering Practice was set up to raise the government’s capabilities in data privacy and drive adoption of Privacy-Enhancing Technologies. Our team has developed whole-of-government tools like Cloak for anonymisation, which is used by 100+ agencies today and has anonymised over 30 million documents to power data analytics and Generative AI use cases, as a Mirage for Synthetic Data GenerationSynthetic Data Generation (SDG) is an upcoming privacy technology that allows for agencies to generate alternative forms of sensitive data that can then be safely utilised and shared. We are seeking a Data Scientist for Mirage, a whole-of-government product for SDG. Mirage is currently offered as a web interface with API integration on the roadmap. We launched in August 2024 and are currently in a growth phase, with active feature development.Job ScopeThis role will be at the intersection of data science, applied machine learning, and software engineering. You will be involved in:1. Model DevelopmentDesign and conduct experiments to evaluate emerging SDG models (e.g., DDPM, ARF, Gaussian Copula).Investigate failure cases (e.g., when models fail with certain data types, size, or cardinality).Tune hyperparameters, refine architectures, and propose new modeling strategies.2. Feature & Product DevelopmentCollaborate with software engineers to build product features that require ML/DS input (e.g., imputation methods, handling of constraints, preprocessing pipelines).Recommend and develop suitable approaches for features like single-/multi-column constraints, imputation strategies, and privacy metrics.3. Diagnostics & DebuggingWork directly with users and the engineering team to diagnose user issues with training failures, poor outputs, or integration challenges.Provide actionable fixes and communicate technical insights in a user-friendly way.4. Documentation & Knowledge SharingWrite user-facing documentation pages. This could include explaining model choice, hyperparameters, and utility/privacy metrics in a user-friendly manner.Translate complex technical Data Science concepts into clear, approachable explanations.5. Collaboration● Work closely with the SWE team (Next.js, FastAPI, AWS) to integrate the generation engine into production-ready systems.● Participate in Agile rituals, code reviews, and design discussions.Requirements1. Bachelor’s degree or higher in Computer Science, Data Science, Business Analytics or a related field, with at least 2-3 years of relevant professional experience. 2. Core Data Science & ML skillsetStrong foundation in machine learning, with hands-on experience in model development and experimentation.Strong programming proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).Ability to analyze model behavior, diagnose training issues, and design experiments to improve performance. 3. Applied Research & ExperimentationFamiliarity with reading, synthesizing, and ability to translate emerging research into practical prototypes. 4. Software EngineeringWorking knowledge of backend development (REST APIs, FastAPI, Flask, or similar).Comfortable working with cloud environments (AWS preferred).Ability to debug and fix software-level issues when they affect ML workflows.Familiarity with Git, CI/CD, and collaborative coding best practices. 5. Nice-to-HavesExperience with privacy-enhancing technologies, anonymisation, synthetic data generation or differential privacy.Familiarity with frontend integration workflows (Next.js/React).Prior experience working in multi-disciplinary product teams. 6. Mindset & CollaborationCuriosity and willingness to learn new domains (esp. data privacy).Strong communication skills to explain technical concepts to both engineers and non-technical stakeholders.Inclination to work in a collaborative, fast-moving Agile environment.