AI Data Scientist

AI Data Scientist ABOUT THE TEAM AND ROLE The investment team is building an AI-enabled research and decision platform that brings together proprietary knowledge, public information, market and alternative data, analytical tools and modern machine-learning capabilities. We are seeking an AI Data Scientist to work directly with portfolio managers, investment professionals and engineers. The role will own high-impact projects from problem definition through model development, deployment, evaluation and ongoing improvement. The successful candidate will combine scientific depth with strong engineering judgement and a practical understanding of how data and AI can improve investment research and decision-making.

Translate investment and research questions into well-defined data-science problems, measurable objectives and practical technical solutions. Develop and deploy machine-learning, statistical and LLM-enabled models for company research, industry analysis, market monitoring, event detection and knowledge discovery. Build robust workflows across the full data lifecycle, including data sourcing, cleaning, transformation, feature engineering, quality checks, modelling and monitoring. Develop retrieval, search and knowledge systems using structured and unstructured data, with rigorous source attribution and evaluation. Design experiments and evaluation frameworks covering model quality, factual accuracy, robustness, latency, cost and user impact. Work with engineers to productionize models and analytical tools through APIs, batch pipelines and monitored applications. Partner closely with investment users to understand workflows, communicate trade-offs and iterate based on evidence and feedback. Identify promising models, research and open-source technologies, and determine when they are—or are not—appropriate for real investment use cases. Improve tooling, documentation and processes to increase reliability, reduce manual work and enable reuse across the team. QUALIFICATIONS / SKILLS REQUIRED PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Engineering, Physics or another highly quantitative field. Minimum 2 years of professional, full-time experience in data science, machine learning, applied AI or a closely related role. Doctoral research alone does not replace the professional-experience requirement. Strong Python proficiency and experience with core scientific and machine-learning libraries such as Pandas, NumPy, scikit-learn and PyTorch or equivalent frameworks. Strong grounding in machine-learning and statistical fundamentals, including problem framing, experimental design, validation, metrics, feature engineering, overfitting and uncertainty. Demonstrated experience delivering at least one end-to-end model or data product used by real stakeholders, from initial scoping through deployment and monitoring. Practical experience with LLMs and modern NLP, including retrieval-augmented generation, embeddings, vector search, prompt or context design and systematic evaluation. Proficiency in SQL and experience working with relational, columnar or document-oriented data systems. Ability to work with messy, incomplete and heterogeneous data while maintaining strong standards for data quality, testing, reproducibility and documentation. Strong written and verbal communication skills, with professional fluency in English and Mandarin.

Experience with financial, market, regulatory or alternative datasets, or with research-intensive decision environments. Experience building data or AI products in cloud environments and deploying APIs, batch jobs, monitoring or feedback loops. Familiarity with knowledge graphs, document processing, browser automation, data visualization or time-series and event-driven modelling. Evidence of technical depth through publications, patents, open-source contributions or substantial production