Data Scientist (AI)

Responsibilities

• Partner with product owners, engineering teams, and industry collaborators across the full product lifecycle—from concept and design through prototyping, acceptance testing, data curation, deployment, operationalisation, industry adoption, and end‑of‑life management.
• Conduct technical assessments on the maturity, viability, and suitability of emerging AI research, technologies, and trends relevant to industry needs.
• Design and develop software components using microservices-based architectures.
• Participate in secure, agile software development processes, including documenting user requirements, technical specifications, and software code throughout the development lifecycle.
• Propose, implement, and validate algorithms while ensuring key functional and non-functional requirements such as transparency, fairness, scalability, security, integration complexity, and operational cost efficiency.
• Produce thought leadership content—including blogs, whitepapers, and technical articles—on AI topics.
• Support technology transfer efforts to industry partners, ensuring smooth adoption and integration.
• Engage and collaborate with research institutes and institutes of higher learning on joint projects, knowledge exchange, and capability development.

Requirements

1. Degree in Engineering, Computer Engineering, Computer Science, Mathematics, Statistics, or a related field.
2. Strong technical expertise in AI, particularly in Computer Vision (CV), Natural Language Processing (NLP), Recommendation Systems, and/or Synthetic AI.
3. Solid understanding of state-of-the-art AI research and production-grade AI technologies, including deployment on mobile devices and IoT platforms.
4. Proficiency in programming languages such as Python and C++.
5. Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.
6. Demonstrated ability to work effectively in cross-functional teams with strong interpersonal and collaboration skills.
7. Excellent communication skills—both written and verbal—with the ability to present complex concepts clearly and engage diverse stakeholders.
8. Strong stakeholder management capabilities, with experience navigating multi-party technical and business environments.