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Senior/ Data Analyst (3 Years Contract)

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Onsite

Senior/ Data Analyst (3 Years Contract)

Ng Teng Fong General Hospital

Senior/ Data Analyst (3 Years Contract)

Skills

DockerPythonData ScienceMicroservicesAWS LambdaData AnalysisMachine LearningAIAWSKubernetesFastAPIAzure

16 days ago

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Important information


Contract type:

Freelance / Contract

Location:

Singapore

Work mode:

Onsite

Published on:

6 October 2026

What they need


We are seeking a Data Scientist to help drive the next phase of our data and AI journey—taking ideas from concept to production‑ready, scalable solutions that deliver tangible impact across clinical and operational settings.

In this role, you will work at the intersection of data, AI, and real‑world healthcare challenges. You will partner closely with clinical and domain stakeholders to design, build, and deploy solutions that are timely, robust, and safe for use in clinically sensitive environments.

Beyond model development, you will play a key role in establishing strong evaluation, deployment, and governance practices, enabling AI solutions to move beyond experimentation and be sustainably adopted at enterprise scale.

The role is hands‑on and delivery‑focused, involving LLM tool development, AWS‑based deployment, evaluation framework design, and close collaboration with clinical teams to support data‑driven decision‑making across the organisation.

We welcome entry level graduates with strong technical foundations and passion for healthcare AI.

Core technical skills:

  • Strong Python proficiency and data science foundations, including exploratory data analysis and visualization, predictive modelling, and machine learning.
  • Willingness to learn cloud technologies (AWS services like Bedrock, ECS, Lambda)
  • Interest in LLMs and generative AI applications (demonstrable projects as an added plus)

Exposure or strong interest in learning:

  • Multi-cloud LLM services (Azure, AWS)
  • Agentic frameworks and tool-use patterns for LLMs
  • Docker containerization and Kubernetes orchestration
  • FastAPI or similar frameworks for building microservices
  • Production-level deployment (MLOps, LLMOps)
  • Evaluation methodologies for LLM outputs (accuracy, hallucination detection, clinical safety)

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