Associate Pricing Analyst

Department: [SBL] Retail Pricing

Employment Type: Permanent - Full Time

Location: Bristol

Compensation: £50,000 - £65,000 / year

Description

What you'll be responsible for:

• Assist in the development and refinement of predictive models for retail pricing. Collaborate with senior analysts to ensure models are robust and accurately reflect retail market conditions.
• Assist in the validation and testing of predictive models for retail pricing to ensure accuracy and reliability.

• Ensure data quality and integrity of data used in predictive models.
• Conduct comprehensive data analysis to identify key risk factors and trends that impact retail pricing for motor insurance products. Utilize advanced statistical techniques to extract insights from large data sets.

• Support Senior Analysts and Managers to evolve the Retail Price Optimisation framework.
• Utilize insights from market research and competitor analysis to refine pricing recommendations.

• Work closely with underwriting, finance, and product development teams to provide actuarial insights and support data-driven decision-making for retail pricing.
• Communicate findings and recommendations clearly to non-actuarial stakeholders.

• Prepare detailed reports and documentation of predictive modelling projects focused on retail pricing.
• Present findings and recommendations to senior management and stakeholders.

• Ensure pricing models and practices comply with relevant regulations and guidelines, including those issued by the Financial Conduct Authority (FCA).

• Stay updated on industry trends, regulatory changes, and advancements in predictive modelling techniques.
• Apply new knowledge to enhance the effectiveness of retail pricing models.

• Contribute to the development and testing of new pricing techniques and algorithms.
• Explore and leverage machine learning tools to enhance predictive accuracy.

What you'll need:

• Bachelor’s degree in Actuarial Science, Mathematics, Statistics, Economics, or a related field. Advanced degrees or additional certifications are a plus.
• Strong analytical and problem-solving skills. Proficiency in using statistical analysis software (e.g., R, SAS, Python, Snowflake) for data analysis and modelling.
• Familiarity with actuarial pricing techniques, including GLMs (Generalized Linear Models) and machine learning algorithms.
• Expertise in Radar and Radar Optimiser is desirable.
• Knowledge of predictive modelling techniques and actuarial principles is required. Experience of modelling within Radar, Emblem and python is an advantage.
• Ability to interpret large, complex datasets and derive actionable insights.
• Excellent verbal and written communication skills. Ability to convey complex actuarial concepts and findings to non-actuarial stakeholders in a clear and concise manner.
• Ability to work effectively with cross-functional teams. Strong interpersonal skills and a collaborative approach to problem-solving.
• High level of accuracy in data analysis and reporting. Meticulous attention to detail in all aspects of actuarial work.

Our Benefits

• Hybrid working – 2 days in the office and 3 days working from home
• 25 days annual leave, rising to 27 days over 2 years’ service and 30 days after 5 years’ service. Plus bank holidays!
• Discretionary annual bonus
• Pension scheme – 5% employee, 6% employer
• Flexible working – we will always consider applications for those who require less than the advertised hours
• Flexi-time
• Healthcare Cash Plan – claim cashback on a variety of everyday healthcare costs
• Electric vehicle – salary sacrifice scheme
• 100’s of exclusive retailer discounts
• Professional wellbeing, health & fitness app - Wrkit
• Enhanced parental leave, including time off for IVF appointments
• Religious bank holidays – if you don’t celebrate Christmas and Easter, you can use these annual leave days on other occasions throughout the year.
• Life Assurance - 4 times your salary
• 25% Car Insurance Discount
• 20% Travel Insurance Discount
• Cycle to Work Scheme
• Employee Referral Scheme
• Community support day