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About the Role
This role involves using machine learning and insurance market knowledge to develop and implement price optimisation for leading retail motor brands. You will create and deliver project plans, communicate results to stakeholders, and contribute to pricing advancements within the Optimisation team.
Responsibilities
- Develop and implement price optimisation across leading retail motor brands.
- Create and deliver project plans.
- Communicate results to stakeholders across the business.
- Review junior team members' work, provide feedback, and suggest improvements.
- Analyze data and create predictive models.
- Build and maintain relationships throughout the Pricing Department and the wider business.
- Interpret analytical outputs and communicate results to technical and non-technical audiences.
- Share technical knowledge in an informative and engaging way to support and mentor junior colleagues.
Requirements
- Experience in a relevant working environment.
- Analytical mind and keen interest in driving data-led decision making.
- Great communicator with the ability to present ideas to stakeholders.
- Proven record in delivering analytical work (financial services background advantageous but not required).
- Strong knowledge and experience of using statistical methods, predictive models, and machine learning techniques.
- Proficiency in Excel and MS Office applications.
- Ability to build and maintain relationships throughout both the Pricing Department and the wider business.
- Proven teamworking and organisation skills to plan and deliver projects, adapting where necessary to external factors.
Skills
- Machine learning techniques
- Insurance market knowledge
- Statistical methods
- Predictive models
- GLMs (Generalised Linear Models)
- GBMs (Gradient Boosting Machines)
- Data handling skills
- Producing summary statistics
- Data transformations
- Data visualisations
- Databricks
- SQL
- PySpark
- Python
- WTW Radar software
- MS Office applications
- Data science as applied to pricing insurance products
- Teamworking
- Organisation skills
- Interpreting analytical outputs
- Communicating results
- Mentoring junior colleagues
- Principles of insurance
- Price optimisation
- UK regulatory environment
- Willis Towers Watson software (Radar Base, Optimiser, Emblem)
- Responsible Artificial Intelligence usage
Location
- London
Work Type
- Hybrid
- Full-Time
- Permanent
Experience Level
- Senior
Salary/Compensations
- Circa £50,000 per year
Benefits
- Global training through the Allianz P&C Academy
- Premium access to platforms like DataCamp, Databricks Academy, and LinkedIn Learning
- Insight sessions
- Flexible buy/sell holiday options
- Hybrid working
- Annual performance related bonus
- Contributory pension scheme
- Development days
- A discount up to 50% on a range of insurance products
- Retail discounts
- Volunteering days
- Flexible working options
- Global career opportunities
- Fantastic career development and training
About the Company
- At Allianz UK, your growth is our priority.
- By working in the Optimisation team, you’ll be putting yourself at the centre of pricing advancements and joining a team with an established pathway for promotion and advancement of your career.
- We are signatories of the ABIs flexible working charter.
- At Allianz, we are committed to fostering building an inclusive, accessible workplace where everyone can thrive.
- As a Disability Confident employer, we welcome applications from disabled and neurodivergent candidates and offer tailored adjustments throughout the recruitment process and in the workplace.
Equal Opportunity
- As a Disability Confident employer, we welcome applications from disabled and neurodivergent candidates and offer tailored adjustments throughout the recruitment process and in the workplace, whether that’s assistive technology, ergonomic equipment, flexible working arrangements, mentoring or coaching.
- If you have a disability or long‑term health condition and meet the essential criteria, you can choose to be considered through our Offer an Interview Scheme.