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- Tasks: Analyse reward data and support pay decisions with insights and reports.
- Company: Join a collaborative company that inspires progress and innovation.
- Benefits: Enjoy hybrid working, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact by helping shape fair and consistent reward practices.
- Qualifications: 1-3 years in analytics; strong Excel skills and a passion for data.
- Other info: Dynamic role with excellent career development in Compensation and Benefits Analytics.
The predicted salary is between 28800 - 48000 £ per year.
With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibilityâ„ .
Reward Data and Analysis- Maintain accurate and up-to-date reward data across internal systems and data sources.
- Gather, clean, and analyze large datasets to support reward and pay decisions.
- Produce regular reports and dashboards on pay, benefits, and workforce trends.
- Support data modelling for salary reviews, bonus calculations, and pay progression.
- Provide data insights and recommendations to support fair and consistent reward practices.
- Own the external market benchmarking exercises and manage participation in appropriate survey data (e.g. Willis Towers Watson, Radford McLagan) for Arch locations outside North America.
- Maintain market awareness and recommend additional surveys and/or new sources as appropriate.
- Help maintain internal pay structures, job levelling, and grading frameworks.
- Support HR requests for market benchmarking.
- Track market trends and support analysis on competitiveness of pay and benefits.
- Support benefits analysis and data collection as appropriate including survey participation and analysis of employee elections and participation.
- Support production of accurate, timely management information for senior stakeholders.
- Help prepare statutory reports such as gender pay gap and other pay transparency requirements.
- Maintain documentation and ensure data accuracy and compliance with GDPR and internal standards.
- 1–3 years’ experience in an analytical or reward-related role (e.g. data analyst, compensation or benefits analyst).
- Strong Excel skills (pivot tables, VLOOKUP, formulas, charts); experience with Power BI or other data visualization tools a plus. VBA or Python a plus.
- Confident working with large, complex datasets.
- Excellent attention to detail, data accuracy, and organization.
- Strong numerical and problem-solving skills with a clear, logical approach.
- Effective communication skills — able to explain insights clearly to non-technical audiences.
- Interest in building a career within Compensation, and Benefits Analytics.
- Degree in Data Analytics, Business, Economics, Finance, Math, or a related discipline (or equivalent experience).
- Formal training in data analytics, Power BI, or advanced Excel an advantage.
Reward Analyst in London employer: Mcneil & Co.
Contact Detail:
Mcneil & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Reward Analyst in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by researching the company and role thoroughly. Understand their values and how your skills as a Reward Analyst can contribute to their goals. Tailor your responses to show you're the perfect fit!
✨Tip Number 3
Practice makes perfect! Conduct mock interviews with friends or use online resources to refine your answers. Focus on articulating your analytical skills and experience with data clearly and confidently.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about joining our team!
We think you need these skills to ace Reward Analyst in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Reward Analyst role. Highlight your analytical skills and any relevant experience with data analysis or compensation. We want to see how your background aligns with what we're looking for!
Show Off Your Skills: Don’t hold back on showcasing your Excel prowess! Mention specific tools like pivot tables and VLOOKUP, and if you’ve dabbled in Power BI or Python, give us a shout. We love seeing those skills in action!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your experience can help us enable possibility. Keep it engaging and personal – we want to get to know you!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be one step closer to joining our collaborative team. Don’t miss out!
How to prepare for a job interview at Mcneil & Co.
✨Know Your Numbers
As a Reward Analyst, you'll be dealing with data all the time. Brush up on your Excel skills, especially pivot tables and VLOOKUPs. Be ready to discuss how you've used these tools in past roles to analyse data and support pay decisions.
✨Showcase Your Analytical Skills
Prepare examples of how you've gathered, cleaned, and analysed large datasets. Think about specific projects where your insights led to better decision-making. This will demonstrate your ability to handle complex data and provide actionable recommendations.
✨Understand Market Trends
Familiarise yourself with current market benchmarking practices and trends in compensation and benefits. Be prepared to discuss how you would approach external benchmarking exercises and what sources you consider reliable for gathering data.
✨Communicate Clearly
You’ll need to explain your findings to non-technical audiences, so practice articulating your insights in simple terms. Consider how you can make complex data relatable and understandable, as this is key to supporting fair reward practices.
