Title: AI Data Scientist Contract: 6 Months with good chance of conversion to permanent
Salary Range: to HKD110,000 (depending on experience)
Summary: As an integral member of the team, Senior Data Scientist is a key position to drive the design, development and deployment of advanced analytics, machine learning and GenAI solutions that unlock growth opportunities, manage risk and enable efficiencies for the business.
What you'll do: - Lead the end-to-end development of machine learning solutions, from problem framing and feature engineering to model training, validation, deployment and monitoring.
- Design and implement GenAI solutions, including retrieval-augmented generation (RAG), prompt engineering, tool/function calling and agent workflows, aligned to business requirements.
- Build and maintain AI engineering components, including scalable data pipelines, model serving patterns and integration with enterprise data sources and platforms.
- Develop robust evaluation pipelines for ML and GenAI systems, including offline/online metrics, test harnesses, regression testing and monitoring for drift and performance.
- Apply responsible AI practices, including explainability, bias/fairness considerations, model governance documentation and control testing where required.
- Partner with product owners, engineers and stakeholders to translate business needs into analytical solutions, communicate trade-offs and drive adoption.
Mentor junior team members through code reviews, technical guidance and best practices in experimentation, engineering and delivery.
Qualifications - Strong programming skills in Python and experience with modern ML libraries (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow).
- Strong understanding of machine learning fundamentals, including supervised and unsupervised learning, model evaluation, feature engineering and model monitoring.
- Hands-on experience delivering GenAI solutions, including prompt design, RAG, embeddings, vector databases and evaluation approaches for LLM-based systems.
- Hands-on experience with AI agent frameworks (e.g., LangGraph, Google ADK) and building agent workflows using tool/function calling.
- Experience building production-grade data and ML pipelines (e.g., orchestration, CI/CD, testing, model packaging and deployment).
- Experience working with cloud platforms such as GCP or Azure and deploying solutions in scalable environments.
- Strong written and verbal communication skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders, in English.
- Team player with strong business and data sensitivity, able to work across product, engineering and risk stakeholders.
What additional skills will be good to have? - Experience in financial services.
- Familiarity with AI governance concepts, including model risk management, responsible AI principles and control frameworks for GenAI.
- Experience with MLOps tooling and observability (e.g., MLflow, model registries, feature stores, monitoring dashboards).
- Experience with distributed data processing (e.g., Spark) and working with large-scale structured and unstructured datasets.
