CV Presentation Guide

This aims to be a step-by-step, comprehensive guide to best presenting your CV – tailored specifically for insurance pricing and data science professionals.

Your CV should be clean, easy to navigate, and logically organized, using clear headings and consistent formatting. White space and readability > trying to cram everything into one page. It should provide an honest and authentic account of your skills, experience and achievements – with a good level of detail which allows prospective employers to easily ascertain your suitability for a role. 

Please read on for an overview of the basics in terms of formatting, structure and layout, and some specific tips on what to include in order to most effectively present your relevant skills and experience.

Formatting

It’s useful to have both an editable MS Word version of your CV as well as a PDF. Alexander Williams Recruitment will ask for a Word version of your CV, in order to add a Cover Sheet whilst maintaining the integrity of the format and keeping this and the CV to one file/document. We use the Cover Sheet to effectively promote elements of candidates’ experience and aspirations, in order to emphasise the suitability of an applicant ‘at a glance’, directly to hiring managers. 

Other formatting tips we would advise are; 

• Use professional fonts like Calibri, Arial, or Helvetica
• Keep font size between 10–12 pt for body text, and 14–16 pt for headings
• Maintain consistent spacing and margins
• Allow for whitespace between sections to boost readability. There are no hard and fast rules that CV’s should be one or even two pages, although we would advise the Professional Summary, Key Skills and at least the most recent Work History feature on the first page
• Try and avoid columns – this can lead to issues parsing CV’s into Applicant Tracking Systems/ ATS’s or if formatting is required
• Save using a clear file name, e.g. ‘johnsmith_CV.pdf’ 

Essential Sections

• Contact Information
• Professional Summary/ Profile
• Key Skills
• Work Experience
• Education

Contact Information:

• Full address or postcode is not required, Town and County are however useful
• Phone number and email address must be provided as a minimum
• Links to LinkedIn, Github or Portfolios can be provided, but not as a replacement for any relevant information that should be included in your CV

Professional Summary:

This is your elevator pitch. In 3-5 lines summarise;

• Who you are professionally 
• Your core strengths
• Your career goals, interests or what you’re looking for

For example:

A results-driven insurance pricing specialist with 10 years’ experience focusing on retail and market pricing for personal lines motor and home insurance. Highly adept at portfolio management, modelling and optimisation, with a proven track record of improving both conversion and retention rates. Seeking to leverage expertise in a strategic, leadership role.

Key Skills:

A brief, concise section covering, at a glance, your relevant key skills; 

• Include hard/ technical skills in context (e.g. Python for Predictive Modelling and Classification)
• Include soft skills you can demonstrate such as leadership and stakeholder management

Work Experience:

• Ensure your work history is presented in reverse chronological order – i.e. most recent/ current role first
• Many people will skip straight to this section first when reading a CV initially. Please do not expect it to be read in the context of the personal profile and key skills you may have already laid out. For example if you have listed Python and Machine Learning in key skills, if you’re discussing modelling responsibilities in your work experience – what techniques did you use? What tools did you use? What was the purpose of the model? It is important to re-iterate these skills in the context of the roles you have performed
• It’s good to ensure certain basic elements specific to insurance, pricing, or analytics/data science are always included for each position, for example;
• What Product Lines you  worked on (e.g. Motor, Home, Pet, P&C, Speciality, Marine)
• What type of Pricing you have did (Portfolio, Market, Retail, Technical/Risk etc.)
• What types of models have built (e.g. Frequency & Severity, Conversion, Retention etc.)
• What tools or techniques you have used (e.g. GLM’s in Emblem, GBM’s in Radar or Python, Forecasting in Excel) – if there is anything commercially sensitive, such as IP relating to any novel modelling techniques, best to leave this out of course
• For the Data Scientists, ensure the business areas you have worked across are clear (e.g. Pricing, Claims, Fraud, Operations, Finance, Underwriting etc.) as well as techniques employed in context (e.g. GenAI/LLM for Chatbots, NLP for Policy Documents, Deep Learning for Image Recognition etc.)
• Focus on impact and results. It’s highly beneficial if you can include achievements in previous roles, and demonstrate positive results from your work. It is totally worth including any quantifiable improvements commercially (e.g. improved Loss Ratios or Conversions), percentage improvement to predictive accuracy of models, or anything that demonstrates the effectiveness of the work you have delivered. Again, best to consider what might be commercially sensitive, but if you are discussing percentage points, typically no issues
• Leadership positions will require conveying a good understanding of the team dynamics and your place within it. If you are managing people, worth including how many, who they are and the team objectives. If you are working towards management, worth including any responsibilities such as coaching, mentoring, technical support or project leadership you provide the team. In both instances, any successes related to these responsibilities should be shared if possible

Education:

• If you are a graduate or early in your career, please include some detail around relevant modules you have studied – e.g. if you are applying for a role that includes predictive modelling, ensure you highlight some details around that statistical modelling or machine learning module you completed

Other considerations

It is totally worth including any Certificates, relevant Self Study, and Awards.

Interests are a bit of a contentious subject, and opinions vary as to whether to include these or if leaving them out they might mitigate any potential bias. In our experience, if someone has the relevant skills and experience for a role, they have never been ruled-out due to anything in the interests section of their CV. However, shared interests can be discovered or curiosity aroused from listed hobbies and activities, which can help build rapport during any subsequent interviews. Essentially the choice of whether to include an interest section is entirely subjective and down to the authors preference.

‘References available of request’ is absolutely fine, these are typically only taken after a candidate has accepted an offer and handed in their notice, and employers will request one from any current employer.

We hope this is a useful guide for any Insurance Pricing, Analytics and Data Science professionals currently writing or reviewing their CV. If you would like any further assistance or a free CV review, please get in touch we will be more than happy to help!