Data Scientist

CEERISK Consulting is a multidisciplinary firm that helps corporations identify engineering risks and develop solutions to control hazards and reduce the impact of costly accidents. The company combines deep knowledge of failure mechanisms across systems and equipment with a strong understanding of evolving global standards. CEERISK focuses on the uncertainties created by complex new technologies and globalization, offering expert risk assessment and management strategies.

By integrating an understanding of emerging technologies, failure modes, and local business and cultural environments, CEERISK brings greater certainty to the construction, energy, and engineering sectors.

This is a full-time, on-site Data Scientist role based in Kingston Upon Thames. The Data Scientist will collect, clean, and analyze structured and unstructured data to support engineering risk assessments and decision-making.

Responsibilities include building statistical and machine learning models, developing predictive analytics, and translating complex data insights into clear recommendations for consultants and clients. The role involves creating data visualizations and reports, collaborating with multidisciplinary teams on risk studies, and contributing to the development of tools and methodologies that improve hazard identification and loss prevention.

The Data Scientist will also help maintain data quality, document analytical processes, and stay current with relevant technologies and standards.

• Strong foundation in Data Science and Data Analytics to design, implement, and evaluate data-driven solutions.
• Proficiency in Statistics and Data Analysis for building robust models and performing rigorous quantitative assessments.
• Experience with Data Visualization to present complex findings clearly to technical and non-technical stakeholders.
• Relevant experience with programming languages and tools commonly used in data science (e.g., Python, R, SQL, modern BI tools).
• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field.
• Ability to work collaboratively in a multidisciplinary environment and communicate technical concepts effectively.
• Knowledge of engineering, construction, or energy domains, and familiarity with risk assessment methodologies, is an advantage.
• Strong problem-solving skills, attention to detail, and commitment to maintaining high data quality and integrity.