Overview
As a Senior Data Scientist, you will source, process, and analyse large structured and unstructured datasets to support quantitative research and portfolio‑related decision making. You will take ownership of selected projects, conducting rigorous analysis and delivering actionable insights to stakeholders.
You will work across the full data lifecycle from dataset scoping and ingestion through to exploratory analysis and prototype pipeline development — leveraging AI‑driven tools to accelerate research and delivery. Projects are largely self‑directed and involve close collaboration with data, analytics, and investment‑focused teams.
Responsibilities
- Collaborate with investment and research stakeholders to identify data opportunities, propose analytical use cases, and evaluate datasets relevant to investment strategies.
- Acquire, clean, transform, and analyse complex structured and unstructured datasets to support quantitative research and decision‑making.
- Define and research key performance indicators (KPIs), perform statistical analyses, and produce clear, well‑structured research outputs.
- Contribute to the design and development of AI‑enabled data platforms that improve data discovery, onboarding, and evaluation.
- Build proof‑of‑concept data products and AI‑driven tools, working with engineering partners to transition prototypes into production.
- Support cross‑functional data initiatives and remain up to date with developments in alternative data, analytics, and applied AI.
Desired Skills and Experience
- At least 4 years of experience in data wrangling, time‑series analysis, statistical analysis, and data visualisation.
- Strong Python programming skills, including experience with libraries such as Pandas, NumPy, Spark, and Matplotlib, and regular use of AI‑assisted coding tools.
- Practical working knowledge of Snowflake, Linux/UNIX environments, Git, and Jira.
- Comfortable working with agentic and AI‑engineering tools as part of a modern data‑science workflow.
- Able to communicate complex technical findings clearly to non‑technical audiences.
- An entrepreneurial and curious mindset, with a strong interest in data‑driven problem solving and investment‑related use cases.
- Strong academic background in a STEM discipline (e.g. mathematics, statistics, engineering, physics, or computer science).
Nice to Have
- Experience collaborating closely with investment or research professionals in fast‑paced environments.
- Experience designing and maintaining ETL pipelines, with solid SQL proficiency.
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