At a Glance
- Tasks: Develop and deploy data science solutions for commercial insurance pricing and underwriting.
- Company: Join a leading insurance firm with a focus on innovation and collaboration.
- Benefits: Enjoy a competitive salary, hybrid work options, and opportunities for professional growth.
- Other info: Be part of a dynamic team with mentorship opportunities and career advancement.
- Why this job: Make a real impact by using data science to enhance pricing strategies and decision-making.
- Qualifications: Experience in data science and strong analytical skills; insurance knowledge is a plus.
The predicted salary is between 60000 - 80000 £ per year.
Role Overview
Primary Details:
- Time Type: full time
- Worker Type: employee
- Location: London / Stafford - Hybrid
Pricing Data Scientist: Develop and deploy data science solutions to support SME commercial insurance pricing, underwriting decisions, and portfolio optimisation.
Responsibilities:
- Identify and prioritise high‐value analytical opportunities that drive measurable improvements in pricing performance (including loss ratio, growth, and retention).
- Lead discussions with business stakeholders to identify how data science can improve decision‐making and outcomes.
- Develop and deploy predictive models to support pricing and underwriting decisions, including risk cost modelling, demand modelling, and price optimisation.
- Translate model outputs into clear pricing recommendations and insights that influence underwriting and portfolio decisions.
- Monitor and evaluate model performance, defining refresh and recalibration requirements.
- Design and prototype analytical tools and applications for business users (e.g. underwriters, claims handlers).
- Work closely with data engineering and data platform teams to source, structure, and prepare data for modelling.
- Lead the development of new analytical propositions to enhance core insurance functions.
- Lead analytical projects and coordinate delivery across teams.
- Mentor junior team members and support their development.
Qualifications:
- Insurance experience is preferred but not essential.
- Strong knowledge of statistical / data mining methods and application in a business environment.
- Proficient in GLMs, machine learning techniques and related disciplines.
- Good understanding of data modelling techniques, tools/language – preferably Python.
- Experience with data science domain, statistical and analytical model development, and implementations.
- Good knowledge of visualization tools like PowerBI, Tableau, etc.
- Experience with insurance pricing tools (such as Emblem and Radar) is preferred but not essential.
- Strong communication, critical thinking, data visualization, financial products, innovation, intentional collaboration, teamwork, mentorship, research analysis, risk management, stakeholder management, and thought leadership.
Equal Employment Opportunity:
QBE is an equal opportunity employer and is required to comply with equal employment opportunity legislation in each jurisdiction it operates.
Pricing Data Scientist in London employer: QBE Insurance Group
At QBE, we pride ourselves on being an excellent employer, offering a dynamic work culture that fosters innovation and collaboration. Our London/Stafford hybrid location provides employees with the flexibility to balance their professional and personal lives while engaging in meaningful projects that drive impactful change in the insurance sector. With ample opportunities for professional growth and mentorship, we empower our team members to excel in their careers and contribute to our mission of enhancing pricing and underwriting decisions through data science.
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We think you need these skills to ace Pricing Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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