Senior Quantitative Analyst, Investment Analytics

Job Title

Department

Overview of Department

Our Investment Teams invest money on behalf of our clients, deciding which companies, stockmarkets and other asset classes to invest in. Financial markets will move up and down over short periods in ways that we cannot control or predict but, by analysing and understanding the key fundamental drivers behind this range of potential investments, we use our experience and judgement to select sound investments capable of producing superior long-term returns for our clients. We are also very mindful that our clients have entrusted us to look after their money so, in addition to delivering good long-term investment returns, we also strive to sustain excellent client service by communicating regularly with them. Our teams are set up to deal with different areas of the world and different types of investments. The teams are deliberately small to foster a collegiate and personal working environment that allows for discussion and debate.

Purpose of Role

Responsibilities

• Producing quantitative insights and bespoke analytics for investors. Analysts will be embedded within investment teams to deliver work tailored to those teams’ processes and philosophies. Analysts will be expected to produce research that makes a material contribution to investment decision-making.
• Building and developing novel datasets from internal and third-party sources to contribute to the analytical foundations of the team. Analysts are expected to maintain an industry-leading knowledge of and capability with investment data, including but not limited to company financial data, factor models, and portfolio analytics tools.
• Cohesive and engaging communication of analytical findings through written reports and dialogue with investment teams. Focusing on actionable conclusions and decision-relevant insights for investors with a clear and detailed outline of analytical methods included for reference where appropriate.
• Developing and enhancing the existing Investment Intelligence platform to scale the impact of the team and make analytical findings accessible across the investment floor. Contributing to the integration of artificial intelligence into investment workflows through the development of tools, semantic views, and agentic loops which can be exposed through our MCP.
• Build trusted relationships across investment, data, technology, risk, trading and operational teams; review work constructively, share knowledge and contribute to client-facing analysis where appropriate.
• An openness to personal growth and continuous learning will be required as well as supporting the development of colleagues within the Investment Analytics team.

What success looks like

• Decision-relevant insight: Quantitative analysis and research provide clear, evidence-based insights that influence investment thinking and make a meaningful contribution to investment decision-making.
• Independent value-add: Opportunities to apply quantitative analysis are identified proactively, with sound judgement and growing independence in determining where and how analytical work can add value for investment teams.
• Ownership and delivery: Complex analytical projects and workstreams are taken from problem definition through to delivery, coordinating effectively with others and maintaining a clear focus on investment or process outcomes.
• Scalable analytical capability: Analytical methods, tools and AI-enabled capabilities extend the value of individual pieces of research, strengthening the Investment Intelligence platform and making insight more accessible across the investment floor.
• Trusted partnership: Strong relationships with investors and colleagues across data, technology, risk, trading and operations enable effective collaboration, constructive challenge and high-quality analytical outcomes.
• Team contribution: Knowledge and expertise are shared openly, supporting the development of colleagues and contributing to a high-performing, continually learning Investment Analytics team.

Qualifications

• A strong academic or equivalent practical foundation in a quantitative discipline, investment analysis, computer science or a related field.
• Full Investment Management Certificate (IMC), or willingness to complete it after appointment.
• CFA, CQF, postgraduate study or another relevant professional qualification would be advantageous but is not required.

Your knowledge and experience

You should bring demonstrated depth in either quantitative investment research or agentic analytical systems, with credible applied capability in the other. We can support growth from a position of strength; we are not looking to fill foundational gaps in both.

• Substantial relevant professional experience, demonstrated through the scope, independence, judgement and impact of your work rather than a fixed number of years.
• Strong applied statistics and quantitative judgement, including method selection, robustness testing and a clear understanding of the difference between correlation, prediction and causality.
• Advanced practical capability in Python and SQL, experience of working with large financial datasets and reproducible analytical workflows.
• Fluent with AI coding tools (e.g. Claude Code, Codex, GitHub Copilot) for development with strong judgment skills to maintain a robust analytical foundation when working with agents. Advanced proficiency with modern software and data engineering practices (testing, design patterns, code review, version control).
• Practical experience working with Large Language Models including context and loop engineering, fine-tuning, and agentic frameworks. Experience building and deploying AI Agents and agentic workflows using open-source frameworks (e.g. OpenAI Agents SDK) and comparable tools or standards such as Model Context Protocol (MCP) for connecting AI agents to tools and data.
• Strong stakeholder management across multiple teams in a large organisation will be beneficial to our collaboration with other business areas.
• Relevant backgrounds may include investment management, quantitative or equity research, data science, analytical engineering, financial technology or applied AI.

The type of candidate that we're looking for:

The ideal candidate will combine analytical depth with a practical desire to improve investment decisions. They will have direct experience in investment management or quantitative equity research as well as strong analytical capabilities. We also welcome candidates from research and data science backgrounds with strong investment knowledge. Essential skills include fluency with AI coding tools, experience with large financial datasets, and practical experience working with Large Language Models to scale analytical outputs. Candidates should demonstrate advanced data literacy, the ability to communicate complex information in a clear and concise manner to multiple different stakeholders, and a readiness for rapid learning. We are looking for candidates that are energized by solving complex, open-ended problems and want to contribute quantitative insight to advance our long-term, active investment approach.

Critical Skills

• Data Literacy
• Improvement Mindset
• Nurture Relationships
• Adaptability

Closing Date

Should you choose to use AI tools to support your application, we ask that you do this thoughtfully. We encourage you to ensure your application reflects your own voice, experience, and motivations. We value authenticity and want to understand your individual strengths and perspectives.

At Baillie Gifford, we are committed to fostering an inclusive and respectful culture in which each of our colleagues can thrive and develop. We believe that our clients are best served by a diverse workforce with the experiences, ideas and perspectives that this brings.

If you are currently working at Baillie Gifford as an employee or contractor please apply to this job from the firm's Workday internal career site.