Data Scientist – Modeller
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Job Description
Senior Data Scientist developing analytical models and AI methodologies for risk and intelligence at Sibylline. Focused on supporting real-world decision-making in security and geopolitical domains.
Responsibilities:
- Designing and maintaining quantitative models, indices, and scoring frameworks for diverse areas such as geopolitical, security, crime, and societal risk
- Developing methodologies that combine structured qualitative judgement with quantitative data
- Using LLMs to support qualitative analysis at scale
- Designing AI-enabled analytical workflows that are transparent, explainable, and suitable for client-facing intelligence products for internal and external products
- Analysing trends across time and geography using heterogeneous data sources
- Validating, stress-testing, and iterating models as new data and intelligence become available
- Clearly documenting assumptions, methodologies, and limitations for both technical and non-technical stakeholders
- Working closely with intelligence analysts, product managers, and software engineers to embed models and AI workflows into production systems
- Contributing to the development of Sibylline’s analytical, modelling, and AI governance standards
Requirements:
- Strong experience in applied data science, quantitative modelling, or analytical research in real-world decision-making contexts
- Proficiency in Python for data analysis and modelling
- Knowledge of SQL and similar technologies for querying and manipulating data
- Experience building indices, indicators, forecasts, or composite scoring systems
- Ability to structure and formalise qualitative judgement and expert assessment
- Hands-on experience using LLMs in applied analytical workflows
- Degree in a quantitative or analytical discipline (e.g. data science, statistics, economics, mathematics, political science with quantitative focus, epidemiology, or similar)
- Familiarity with model interpretability, explainability, and AI governance in applied settings
- Experience working with intelligence, risk, or security-related datasets.
Benefits:
- Career progression opportunities
- Exposure to high-profile clients
- Flexible working arrangements.
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