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:
• 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).
• Experience of using GPU-accelerated deep learning frameworks (such as PyTorch and TensorFlow).
• 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.