Year in Industry – Asset Allocation Programme – London – 2027

Overview

Join L&G’s Year in Industry placement to develop skills in asset allocation for multi-asset funds. You’ll learn from economists, strategists and portfolio managers, while contributing to research, market analysis and client communications. The program blends hands-on work, mentoring and cross-functional learning within a supportive Future Talent community. This is a chance to build a foundation for a career in investment management and impact-focused, long-term value creation.

Pay / Benefits

• Life assurance
• At least 25 days holiday, plus public holidays; 26 days after 2 years’ service
• Discounts on products and partner retailers
• Net-zero carbon workplaces by 2030
• Flexible working options
• Opportunity to develop skills and network within L&G

Responsibilities

• Contribute to research and analysis that inform asset allocation decisions and portfolio positioning
• Engage with live investment questions and track daily market developments
• Support day-to-day management of multi-asset portfolios
• Assist in client communications about portfolios and investment activity
• Take ownership of longer-term projects to develop quantitative and investment skills
• Analyze financial and time-series data using qualitative and quantitative techniques
• Use Python and other investment-team tools to support analysis
• Present findings and develop communication skills

Key requirements

• On track for a 2.1
• Penultimate-year student on a four-year course, graduating 2028
• Grade B or above in A-level Maths
• GCSE Maths and English Grade 5
• Open to learning about new topics and ways of working
• Enjoy solving problems and learning from challenges
• Collaborate effectively with others and learn from diverse perspectives
• Willing to share ideas and learn from feedback
• Care about doing a good job and continuous improvement
• Willing to develop ways of organizing work and managing priorities
• Some coding experience

• Collaborative and inclusive mindset
• Curiosity and willingness to learn
• Questioning and receptive to feedback
• Python
• Microsoft Excel
• Basic data analysis of time-series data

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