Job Title: Data Scientist (Public Sector)
Duration: 12 months
Location: Punggol
Salary:
Work Timing: 8.30am – 6.00pm (Monday to Thursday), 8.30am - 5.30pm (Friday)
Eligibility: Only Singaporeans
Main Responsibilities:
Design 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.
Feature & Product Development:
Collaborate 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.
Diagnostics & Debugging:
Work 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.
Documentation & Knowledge Sharing:
Write 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.
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.
Requirements:
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.
Strong 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.
Familiarity with reading, synthesizing, and ability to translate emerging research into practical prototypes.
Software Engineering:
Working 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.
Nice-to-Haves:
Experience 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.
Mindset & Collaboration:
Curiosity 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.
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