AI Data Scientist

About the role

The investment team is building an AI‑enabled research and decision platform that combines proprietary knowledge, public data, market and alternative data, analytical tools and modern machine‑learning capabilities. As an AI Data Scientist you will work directly with portfolio managers, investment professionals and engineers to turn research questions into high‑impact AI solutions.

Key responsibilities

• Translate investment and research questions into well‑defined data‑science problems with measurable objectives.
• Design, develop and deploy machine‑learning, statistical and LLM‑enabled models for company research, industry analysis, market monitoring, event detection and knowledge discovery.
• Build robust end‑to‑end data pipelines covering sourcing, cleaning, transformation, feature engineering, quality checks, modelling and monitoring.
• Create retrieval, search and knowledge systems using structured and unstructured data with rigorous source attribution.
• Design experiments and evaluation frameworks covering model quality, factual accuracy, robustness, latency, cost and user impact.
• Collaborate with engineers to productionise models via APIs, batch pipelines and monitored applications.
• Partner with investment users to understand workflows, communicate trade‑offs and iterate based on feedback.
• Identify promising research, open‑source technologies and assess their suitability for real investment use cases.

Required profile

• PhD in Computer Science, Machine Learning, AI, Statistics, Applied Mathematics, Engineering, Physics or a related quantitative field.
• Minimum 2 years of full‑time professional experience in data science, machine learning or applied AI.
• Strong grounding in machine‑learning and statistical fundamentals, experimental design and model validation.
• Proven track record of delivering at least one end‑to‑end model or data product used by real stakeholders.

Required skills

• Python programming
• Pandas library
• NumPy library
• scikit‑learn library
• PyTorch (or equivalent deep‑learning framework)
• Experience with large language models (LLMs) and modern NLP techniques