#EG Junior Data Scientist
HybridSingapore, Singapore
Job Summary
Translate customer pain points into problem statements and propose machine learning approaches to automate complex business processes. Develop and manage the end-to-end ML lifecycle, including data cleaning, feature engineering, model training, and production deployment. Write clean Python code for end-to-end pipelines, conduct code reviews, and design visualizations to explain actionable insights to business users. Present statistically sound model validations and engage with stakeholders to refine requirements and surface risks early. Support client workshops, sprint demos, and retrospectives while communicating technical trade-offs in clear, non-jargon language. Lead high-impact AI management consulting programs for major enterprises and public sector clients to drive real business transformation.
Required Qualifications
- 2–4 years of hands-on ML and data science solution development experience
- Degree in Computer Science, Data Science, Statistics, Mathematics, Information Systems, or equivalent quantitative discipline
- Proven delivery of complex end-to-end AI/ML projects
- Strong Python programming: Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow; comfortable writing modular, testable code
- Solid understanding of ML fundamentals: feature engineering, supervised and unsupervised modelling techniques, model evaluation, bias-variance trade-off, hyperparameter optimisation
- Experience with Git, CI/CD, containerisation (Docker) and MLOps processes
- Working knowledge of MLflow or equivalent for experiment tracking and model management
- Possess good communications skills to understand customers' core business objectives and present technical solutions in a clear, understandable way
- Good critical thinking and problem-solving abilities
- Enthusiasm for building and implementing machine learning solutions in industry production settings
Desired Qualifications
- (Good to have) Generative AI solution experience: building GenAI-based chatbots and applications, RAG systems, working with LLM APIs, developing and evaluating quality prompt templates, working with vector databases and embedding models for semantic search and retrieval (e.g. Milvus, pgvector)
- (Good to have) Experience working with cloud platforms like AWS Sagemaker or Azure ML
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