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Senior Data Scientist - Cyber Risk Modelling

Job LocationBristol
EducationNot Mentioned
Salary£70,000 - £80,000 per annum
IndustryNot Mentioned
Functional AreaNot Mentioned
Job TypePermanent , full-time

Job Description

Position: Senior Data Scientist - Cyber Risk Quantitative Risk ModellerLocation: BristolOur client, a pioneering reinsurance agency based in Bristol, UK, is seeking a highly skilled and experienced Senior Data Scientist - Cyber Risk Quantitative Risk Modeller to join their dynamic modelling team. They specialise in the cutting-edge domain ofcyber risk and aim to redefine the landscape of cyber risk assessment and management.As a Senior Data Scientist, reporting into the Head of Data Science & Modelling, you will play a pivotal role in the development and operationalization of our clients proprietary stochastic cyber risk model. This position offers an exciting opportunityto contribute significantly to the advancement of analytical capabilities and the broader field of cyber risk modelling.Responsibilities:- Model Development and Operation: Be a key member of the team responsible for designing, developing, refining, and executing the stochastic cyber risk model, ensuring its accuracy, performance, and scalability.- Data Analysis: Perform complex data analysis to extract insights and identify trends in cyber risk using statistical and machine learning techniques.- Operationalization: Translate model insights into actionable strategies and tools for internal and external stakeholders.- Collaboration: Work closely with other team members, including underwriters, engineers, and cyber risk analysts, to integrate the cyber risk model with other systems and processes.- Innovation: Stay updated with the latest developments in data science, cyber security, and risk modelling and incorporate innovative techniques and technologies into our models.Qualifications:- Experience: Minimum of 5 years of experience as a data scientist/quantitative risk modeller, with a proven track record of operationalizing complex models and analytics.- Education: A degree in Computer Science, Engineering, Statistics, Mathematics, or a related field. Advanced degrees (MSc or PhD) are preferred.- Technical Skills: Expertise in applied machine learning, probability, statistics, and quantitative risk modelling. High proficiency in Python and SQL, with experience in big data technologies and tools (Databricks and Pyspark preferred).Familiarity with agile software development processes.- Industry Knowledge: Experience in insurance, cyber risk, or related domains is ideal. Understanding of the reinsurance industry and its challenges is a plus.- Soft Skills: Excellent problem-solving abilities, strong communication skills, and the capacity to work effectively in a team-oriented environment.Benefits:- Impact: Make a tangible impact on the future of cyber risk management and reinsurance.- Innovation: Work at the forefront of data science and cyber security, with opportunities to innovate and challenge the status quo.- Growth: Benefit from opportunities for professional development and advancement in a rapidly growing company.- Culture: Join a collaborative, supportive, and forward-thinking team that values innovation and excellence.Salary: £70,000 to £80,000 per yearContract Type: PermanentWorking Pattern: Full TimeAdditional Perks: Health insurance, Hybrid working, Life assurance, Private Medical, 5% pension, 28 days annual leave plus bank holidays, Collaborative workingIf you have the skills, experience, and passion to excel in this role, apply now and be a part of our clients groundbreaking work in the field of cyber risk assessment and management.Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities to apply. Adecco is committed to building a supportive environmentfor you to explore the next steps in your career. If you require reasonable adjustments at any stage, please let us know and we will be happy to support you.

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