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For Companies At a Glance
- Tasks: Drive innovation in equity strategies through data analysis and collaboration.
- Company: Join a prestigious global investment firm known for cutting-edge technology and research.
- Benefits: Enjoy a dynamic work environment with opportunities for global collaboration.
- Why this job: Be part of a team where data drives decisions and impacts investment strategies.
- Qualifications: Bachelor’s degree in a scientific field; strong SQL and Python skills required.
- Other info: Must be eligible to work in the UK; no remote work options available.
The predicted salary is between 43200 - 72000 £ per year.
Job Description
Company Insight:
This prestigious global investment management firm is expanding its London office, seeking intellectually curious individuals to drive innovation in equities strategies. Leveraging proprietary cutting-edge technology and rigorous scientific research, the company excels in trading, technology, and operations, and is now looking to expand their discretionary organisation. Data is not a support function here but drives the firm’s approach, with data scientists having direct and tangible communication with PMs and traders. With a true emphasis on global collaboration, their investment, technology, and operations teams are aligned functionally around the world.
Your Role:
- Develop insights and generate ideas for the equity fundamental research team using data-driven approaches.
- Collaborate across the firm to exchange results, feedback, and custom tools effectively.
- Conduct tasks such as KPI estimation and projection for companies or sectors, hypothesis testing through data analysis, building screening tools, and assessing new and existing datasets for relevance and quality.
- Gain a deep understanding of the fundamental models used by analysts and create data-driven variables to enhance these models.
- Assess the value and relevance of new datasets for specific sectors or stocks.
- Execute data-driven studies to validate or refute hypotheses for specific research ideas.
Experience/Skills Required:
- Bachelor’s degree in a scientific field
- Strong programming skills, proficiency in SQL and Python required
- Experience in analysing complex data sets and research experience with alternative data is a plus
- Industry experience in fundamental analysis preferred
- Minimum 2 years of experience in a buy-side firm
- Experience with healthcare or energy data sectors preferred
Pre-Application:
- Please do not apply if you are looking for a contract or remote work
- You must be eligible to live and work in the UK, without requiring sponsorship
- Please ensure you meet the required experience section prior to applying
- Allow 1-5 working days for a response to any job enquiry
Your application is subject to our privacy policy, found here: https://www.thurnpartners.com/privacy-policy
Contact Detail:
Thurn Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Equity Sector Data Analyst, London
✨Tip Number 1
Familiarise yourself with the latest trends in equity research and data analysis. Understanding current methodologies and technologies used in the industry will help you speak confidently about your insights during interviews.
✨Tip Number 2
Network with professionals in the investment management sector, especially those working in data analysis roles. Attend industry events or webinars to make connections that could lead to referrals or insider information about the company culture.
✨Tip Number 3
Brush up on your SQL and Python skills by working on real-world projects or contributing to open-source initiatives. Demonstrating your programming proficiency through practical examples can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss specific datasets you've worked with, particularly in the healthcare or energy sectors. Being able to articulate how you've assessed the relevance and quality of these datasets will showcase your analytical capabilities.
We think you need these skills to ace Equity Sector Data Analyst, London
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities of an Equity Sector Data Analyst. Familiarise yourself with the key tasks mentioned in the job description, such as KPI estimation, hypothesis testing, and data-driven studies.
Tailor Your CV: Customise your CV to highlight relevant experience and skills that align with the job requirements. Emphasise your programming skills in SQL and Python, as well as any experience with complex data sets or fundamental analysis.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data analysis and investment management. Mention specific examples of how you've used data-driven approaches in previous roles and how they relate to the responsibilities of this position.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any spelling or grammatical errors, and ensure that all information is accurate and clearly presented. A polished application reflects your attention to detail.
How to prepare for a job interview at Thurn Partners
✨Showcase Your Analytical Skills
Prepare to discuss specific examples of how you've used data analysis in your previous roles. Highlight any experience with SQL and Python, as these are crucial for the position. Be ready to explain your thought process and the impact of your findings.
✨Understand the Company’s Approach
Familiarise yourself with the firm's investment strategies and how they leverage data. This will help you align your answers with their values and demonstrate your genuine interest in their innovative approach to equity research.
✨Prepare for Technical Questions
Expect technical questions related to data analysis, KPI estimation, and hypothesis testing. Brush up on relevant concepts and be prepared to solve problems on the spot, showcasing your critical thinking and problem-solving abilities.
✨Emphasise Collaboration Experience
Since the role involves collaboration across teams, share examples of how you've successfully worked with others in the past. Discuss how you exchanged feedback and tools effectively, as this will highlight your ability to thrive in a collaborative environment.
