- Interact closely with research project team which includes academics, clinicians and industry partners.
- Conceptualise, develop, integrate and maintain in-house AI and Cognitive solution.
- Apply state-of-the-art AI algorithms, explore alternatives and build working prototypes. You will also deploy systems and scale the solution.
- Create models, train, and leverage machine learning APIs to build solution with embedded intelligence in use-cases like Chatbots.
- Follow Agile/Lean/Scrum software methodologies for development. Certified ScrumMaster (CSM) will be an advantage.
- To research and be updated on the state of the art on AI, data engineering and data science practices. Be plugged into the wider community on these key areas.
1. Master’s degree in AI, Machine Learning, Statistics, Computing, Biostatistics, Information Technology, Social Sciences or related fields.
2. Experience preferably in
- Open source DevOps tools and software-as-a-service solutions
- Development and implementation of AI and machine learning algorithms
- AI or deep learning work at a commercial company or research laboratory
Experience in building deep learning models with popular frameworks e.g. TensorFlow. Knowledge of common machine learning techniques and key parameters that affect performance.
3. At least 2-3 years hands-on experience in developing applications and with various data analysis and visualisation tools. Experience with R and/or Python.
4. Proven experience with track record of processing and extracting value from large data sets. i.e. Data analysis and discovery with new insights. Understand relevant statistical measures (development and evaluation data sets, confidence intervals, significance of error measurements, etc)
5. Data warehousing/RDBMS experience with SQL Server, Oracle, MySQL, etc.
6. Demonstration of self-learning through hackathons, MOOC or open user-group and conference participation. Self-motivation with side projects, Kaggle competitions, etc.
7. Proficient in running deep learning modules on Cloud platforms like Azure or Google Cloud. Able to use Virtual Machine instances on these platforms. Experience in test automation, test-driven development and cloud-based implantation will be an advantage.
8. Strong system-building and management skills and keen interest in working with emerging technologies and frameworks.
9. Good awareness of security considerations/best practices for web-facing applications. Having a strong interest in learning new security technologies and tools are essential.
10. Good data skills; able to combine technical visualisation libraries (e.g. Python) with more user-friendly tools like Tableau. Ability to apply good design principles to visualisation, dashboard and reports
11. Strong communication skills and early adopter and growth mindset is highly regarded.
Ability to present and explain difficult concepts clearly and concisely in plain English.