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For Companies At a Glance
- Tasks: Join a team to clean and prepare insurance data for cutting-edge risk models.
- Company: Be part of a pioneering company in cyber risk using advanced techniques.
- Benefits: Enjoy a hybrid work model with competitive salary and growth opportunities.
- Why this job: Make a real impact in cyber risk while working with innovative data solutions.
- Qualifications: 2-4 years in a data-driven role with strong Python and Pandas skills required.
- Other info: Bonus points for experience in financial services or familiarity with Databricks.
The predicted salary is between 28800 - 36000 £ per year.
Job Description
Data Scientist or Junior Data Scientist – Hybrid (Bristol-based 2-3 days a week) Insurance
?? £40,000 – £45,000 (up to £50,000 for exceptional candidates)
Strong skills in Python & Pandas with at least 2 years experience in a commercial Data Driven role
Are you passionate about data and ready to make a real impact in the world of cyber risk?
We're working with a forward-thinking company that's pioneering cyber risk using advanced stochastic techniques, and they're on the lookout for a Data Scientist or Junior Data Scientist to join their growing team. This is a brilliant opportunity for someone with a sharp analytical mind and solid Python skills, who enjoys building efficient data pipelines and uncovering insights from complex datasets.
?? The Role
You'll play a key role in cleaning, enriching, and preparing insurance portfolio data that feeds into cutting-edge risk models. From day one, you'll work closely with modellers, data scientists, and engineering experts to ensure high data quality and process efficiency. The work you do will directly influence insights into cyber exposure and risk trends.
??? Key Responsibilities
- Clean, validate, and standardise large insurance datasets using Python (especially Pandas)
- Develop and refine internal tools and utilities for data cleaning workflows
- Support the integration of LLMs to automate data prep, including prompt engineering and model evaluation
- Generate insightful data reports related to insurance exposure and risk events
- Communicate findings clearly to both technical and non-technical teams
- Apply software engineering practices to improve data systems and pipelines
- Continuously improve data workflows, quality, and ingestion pipelines (ETL)
- Stay ahead of trends in data science, reinsurance, and cyber risk
�? What We're Looking For
- 2-4 years in a data-driven or technical role
- Strong skills in Python and Pandas is a must
- Data ingestion using ETL pipelines experience
- A background in financial services is preferred, but not essential.
- Insurance experience a bonus
- Excellent attention to detail and a strong sense of data quality
- Commercial experience blending data engineering and data science approaches
- Curious, adaptable, and a natural problem solver
Bonus points for:
- Experience in financial services, insurance, or reinsurance
- Familiarity with Databricks, Git, PySpark or SQL
- Exposure to cyber risk or large-scale modelling environments
?? Ready to Apply for this exciting Data Scientist role?
Send your CV to – I'd love to hear from you!
Data Scientist / Junior Data Scientist Python, Pandas employer: Adecco
Contact Detail:
Adecco Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist / Junior Data Scientist Python, Pandas
✨Tip Number 1
Familiarise yourself with the latest trends in data science, especially in the context of cyber risk and insurance. This will not only help you understand the industry better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Showcase your Python and Pandas skills through practical examples. Consider working on personal projects or contributing to open-source projects that demonstrate your ability to clean and analyse datasets effectively.
✨Tip Number 3
Network with professionals in the insurance and data science fields. Attend relevant meetups or webinars to connect with others who can provide insights or even refer you to opportunities within their companies.
✨Tip Number 4
Prepare to discuss your experience with ETL pipelines and data workflows. Be ready to explain how you've improved data quality and efficiency in past roles, as this is a key aspect of the job you're applying for.
We think you need these skills to ace Data Scientist / Junior Data Scientist Python, Pandas
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and Pandas, as well as any relevant data-driven roles. Use specific examples to demonstrate your skills in cleaning and preparing datasets.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data and how it relates to cyber risk. Mention your analytical mindset and provide examples of how you've contributed to data quality and process efficiency in previous roles.
Showcase Relevant Projects: If you have worked on projects involving ETL pipelines or data reporting, be sure to include these in your application. Highlight any tools or technologies you used, such as Databricks or SQL, to demonstrate your technical capabilities.
Prepare for Technical Questions: Anticipate questions related to your experience with data cleaning and analysis. Be ready to discuss specific challenges you've faced and how you overcame them, particularly in relation to insurance datasets or risk modelling.
How to prepare for a job interview at Adecco
✨Showcase Your Python and Pandas Skills
Make sure to highlight your experience with Python and Pandas during the interview. Be prepared to discuss specific projects where you've used these tools, and consider bringing examples of your work or code snippets to demonstrate your proficiency.
✨Understand the Role of Data in Cyber Risk
Familiarise yourself with how data science applies to cyber risk, especially in the insurance sector. Research the company's approach to using data for risk modelling and be ready to discuss how you can contribute to their goals.
✨Prepare for Technical Questions
Expect technical questions related to data cleaning, ETL processes, and data pipelines. Brush up on your knowledge of best practices in data quality and be ready to explain your thought process when solving data-related problems.
✨Communicate Clearly with Examples
Since you'll be working with both technical and non-technical teams, practice explaining complex concepts in simple terms. Use examples from your past experiences to illustrate how you've effectively communicated findings to diverse audiences.
