Junior Data Scientist

About the role

Actxa, a deep tech company headquartered in Singapore, uses artificial intelligence to build preventive healthcare solutions that deliver personalized health insights on physical activity, sleep, body composition, stress and recovery, heart health and blood glucose. It is looking for a Junior Data Scientist to turn wearable sensor data into health insights and to support research with clinical and academic partners.

Key responsibilities

• Research and develop algorithms that turn wearable sensor data (PPG, accelerometer, heart rate variability) into insights on activity, sleep, stress, recovery and heart health.
• Build and improve models for blood glucose estimation and nutrition, including food recognition and nutrition data pipelines.
• Develop generative AI and LLM-based features such as personalized health insights, conversational coaching and behavioral nudging.
• Help design and run research studies with clinical and academic partners, covering protocol preparation, data collection and data quality checks.
• Clean, organize and analyze health datasets using statistical modelling, machine learning and deep learning.
• Test and validate algorithm and AI model outputs against reference data and document methods and results.
• Work with software engineers to bring models into Actxa's apps and products.
• Use AI-assisted tools in day-to-day work while critically validating their outputs.
• Contribute to research publications and invention disclosures.

Required profile

• Bachelor's or Master's degree in Data Science, Computer Science, Computer Engineering, Biomedical Engineering, Statistics or a related discipline.
• 0 to 2 years of relevant experience; fresh graduates with strong internship or project experience are welcome.
• Keen interest in digital health, wellness and preventive healthcare.
• Able to explain findings clearly to non-technical colleagues.
• Positive, adaptable and comfortable in a small team where priorities can shift.

Required skills

• Good working knowledge of Python and SQL, and libraries such as pandas, NumPy, scikit-learn and PyTorch or TensorFlow.
• Solid foundation in statistics and machine learning.
• Plus: signal processing or time-series data from wearables, familiarity with LLMs, prompt engineering or frameworks such as Hugging Face, LangChain or LLM APIs, exposure to clinical or health research or glucose and nutrition data.