At a Glance
- Tasks: Design and develop analytical models for real-world security and risk decision-making.
- Company: Join Sibylline, a leading consultancy in the security sector with a dynamic work culture.
- Benefits: Remote work, competitive salary, and opportunities for career progression.
- Other info: Collaborate with top analysts and work on high-profile projects globally.
- Why this job: Make an impact using AI and data science in a fast-paced, innovative environment.
- Qualifications: Degree in a quantitative field and experience in data science or analytical research.
The predicted salary is between 36000 - 60000 € per year.
About Sibylline
Sibylline is a leading intelligence and strategic risk consultancy in the security sector. Since 2010 we have supported businesses, governments and NGOs by providing high-quality risk analysis, due diligence and consultancy services. The firm provides an innovative, entrepreneurial and fast-growing working environment, offering employees exposure to high-profile clients and challenges. Sibylline offers opportunities for career progression and aims to help employees build their own profiles as well-regarded analysts within the broader industry.
Key attributes of Sibylline employees:
- Self-motivated, and auto-improving individuals who can couple initiative and boldness with good judgement
- Excellent written and spoken English
- Clarity of thought and analytical flair
- Strong, demonstrable interest in security and intelligence
- Ability to work under pressure, demonstrate leadership when required but also collaborate effectively in teams
- Excellent attention to detail
Role Summary
We are building Sibylline’s core data and intelligence platform to support real-world security, geopolitical, and risk decision-making. We are seeking a Senior Data Scientist specialising in applied modelling to develop analytical models and AI-enabled methodologies that combine quantitative data, qualitative assessment, and expert judgement. This role focuses on building indices, indicators, trend analyses, and structured assessments across areas such as geopolitics, crime, security, public health, and societal risk. A key part of the role involves using Large Language Models (LLMs) to support qualitative analysis at scale to extract signals from text, structure expert assessments, enrich data, and support transparent, auditable analytical workflows. Your work will be embedded directly into a developing enterprise SaaS intelligence platform to be used by analysts and clients globally. This is a hands-on role suited to someone who enjoys working at the intersection of data science, qualitative analysis, and applied AI, and who is comfortable operating in complex, ambiguous real-world domains. This is a remote role working 9 am - 5 pm, Monday to Friday, requiring occasional travel to London. The London office is available to work in if desired.
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 (e.g. text analysis, signal extraction, summarisation, classification, enrichment)
- 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 (structured, semi-structured, and unstructured)
- 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
Knowledge, Skills, And Abilities
- Strong experience in applied data science, quantitative modelling, or analytical research in real-world decision-making contexts
- Desired: prior experience in geopolitical or geoeconomic modelling
- Proficiency in Python for data analysis and modelling
- Working knowledge of SQL and similar technologies for querying and manipulating data
- Desirable: knowledge of frontend development including React, Typescript
- 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 (e.g. prompt design, evaluation, chaining, or hybrid human–AI analysis)
- Strong statistical reasoning and data analysis skills
- Proficiency in Python (or similar) for data analysis and modelling
- Experience working with time-series, geospatial, event-based, or text data is highly desirable
- Prior experience in geopolitical or geoeconomic modelling is highly desirable
- Strong interest in geopolitics, security, crime, public health, or societal risk
- Comfortable working in a trusted environment and eligible for background screening
Qualifications
- Degree in a quantitative or analytical discipline (e.g. data science, statistics, economics, mathematics, political science with quantitative focus, epidemiology, or similar)
- Experience working with intelligence, risk, or security-related datasets
- Familiarity with model interpretability, explainability, and AI governance in applied settings
- Experience collaborating with software engineering teams to productionise analytical and AI-driven outputs
Interview Process
- Initial call with our Talent Acquisition team member
- 30-minute video call with the hiring manager
- Home task
- Panel interview with some of the team members and hiring managers at Sibylline
Research indicates that certain groups are less likely to apply for a position unless they meet every single requirement. If you feel you meet some of the requirements and can offer a unique perspective to this role, we strongly encourage you to apply—you might be the perfect fit we’re looking for!
Sibylline is committed to the recruitment and selection of candidates without regard for sexual orientation, gender, ethnicity, age, political beliefs, culture, and lifestyle. We are committed to fostering a business culture that reflects these values and promotes equal opportunity.
Data Scientist/Modeller in London employer: Sibylline
Sibylline is an exceptional employer that fosters a dynamic and innovative work culture, providing employees with the opportunity to engage with high-profile clients in the security sector. With a strong emphasis on career progression and personal development, team members are encouraged to build their profiles as respected analysts while working remotely or from our London office. The company values diversity and inclusion, ensuring a supportive environment where self-motivated individuals can thrive and contribute to meaningful projects.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist/Modeller in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by researching Sibylline and understanding their work in security and intelligence. Tailor your responses to show how your skills in data science and modelling can directly contribute to their projects.
✨Tip Number 3
Practice your problem-solving skills! Be ready to tackle real-world scenarios during interviews. Think about how you would apply your data science expertise to analyse trends in geopolitics or societal risk.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Sibylline team.
We think you need these skills to ace Data Scientist/Modeller in London
Some tips for your application 🫡
Show Your Passion:When writing your application, let your enthusiasm for data science and the security sector shine through. We want to see that you’re genuinely interested in the role and how your skills can contribute to our mission at Sibylline.
Be Clear and Concise:Make sure your application is easy to read and straight to the point. Use clear language to describe your experience and skills, especially those related to quantitative modelling and AI methodologies. We appreciate clarity of thought!
Tailor Your Application:Don’t just send a generic application! Tailor your CV and cover letter to highlight relevant experiences that align with the job description. Show us how your background in applied data science makes you a great fit for this role.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re proactive and keen to join our team!
How to prepare for a job interview at Sibylline
✨Know Your Data Science Stuff
Make sure you brush up on your data science skills, especially in applied modelling and quantitative analysis. Be ready to discuss your experience with Python, SQL, and any relevant projects you've worked on that relate to geopolitical or societal risk.
✨Show Your Interest in Security and Intelligence
Sibylline values a strong interest in security and intelligence. Prepare to talk about current events or trends in these areas and how they relate to data science. This will show your passion and understanding of the field.
✨Prepare for Technical Questions
Expect technical questions during the interview, especially around LLMs and analytical workflows. Brush up on your knowledge of model interpretability and be ready to explain your thought process when it comes to designing models and methodologies.
✨Demonstrate Team Collaboration Skills
Since the role involves working closely with analysts and engineers, be prepared to discuss your experience in team settings. Share examples of how you've collaborated effectively in the past, especially under pressure or in complex situations.
