Information Technology - Data Scientist (Data Science Track)

SIA has multiple positions for junior and senior data scientists to drive our AI, data science and business analytics initiatives.

Key Responsibilities include:

• Member of an in-house AI and data science development team that works on AI (including areas in generative AI, autonomous agent, NLP, computer vision and recommender system), mathematical optimization, game theory, and experimental design.
• Work closely with business stakeholders to create impactful and intelligent features/services in AI, data science and data analytics. Propose and build scalable ML/DL solutions. Deploy them as API microservices for use by software applications and business users for faster and more effective decision making.
• Oversee the technical work of external technology partners and provide them datasets to deliver products/services in AI and data science. Support business users in the assessment/ validation of partner-supplied prediction models and in their deployment to production cloud.
• Work closely with application development teams to operationalize and integrate AI/ML capabilities in API microservices.
• Note: You could be posted to any subsidiary in SIA Group.

Requirements

• BS in Computer Science, Mathematics, Statistics, Physics or related discipline is required. PhD and MS degrees related to machine learning and other AI disciplines are preferred.
• Intermediate programming skills in Python. Conversant with algorithm design/analysis, data structure and SQL. Familiarity with functional/object-oriented software development using modern programming languages such as Scala, TypeScript/JavaScript, Java and C# is a plus.
• Exposure in the use of workflow/map-reduce and stream processing systems such as Spark and Kafka for big-data processing.
• Relevant internship or industry experience as a hands-on data scientist/AI engineer in shallow and/or deep learning (you should be very comfortable with most topics covered in the free undergraduate textbook by Gareth James, “An Introduction to Statistical Learning – with Applications in Python”, 2023). Additional exposure in some of the following areas is a plus:

1. Use of recent proprietary/open-source LLMs through prompt engineering (such as OpenAI ChatGPT and Bing Chat). Familiarity with LLM application/data frameworks (such as LlamaIndex and LangChain).
2. Experience of using GPU-accelerated deep learning frameworks (such as PyTorch and TensorFlow).
3. Familiarity with Bayesian statistics/inference and Bayesian/causal networks for probabilistic reasoning.

• Some hands-on experience of AWS, Azure, GCP or other public cloud environment.
• Good interpersonal and communication skills for working with both technical staff and non-technical business users.
• Experience with Agile/Scrum/Kanban methodologies is a plus.