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- Tasks: Drive innovation in equity strategies through data analysis and collaboration.
- Company: Join a prestigious global investment management firm expanding its London office.
- Benefits: Enjoy a dynamic work environment with opportunities for global collaboration.
- Why this job: Be part of a data-driven culture that values your insights and creativity.
- Qualifications: Bachelor’s degree in a scientific field; strong SQL and Python skills required.
- Other info: No remote work; must be eligible to live and work in the UK.
The predicted salary is between 43200 - 72000 £ per year.
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 fieldStrong programming skills, proficiency in SQL and Python requiredExperience in analysing complex data sets and research experience with alternative data is a plusIndustry experience in fundamental analysis preferredMinimum 2 years of experience in a buy-side firmExperience with healthcare or energy data sectors preferredPre-Application:Please do not apply if you are looking for a contract or remote workYou must be eligible to live and work in the UK, without requiring sponsorshipPlease ensure you meet the required experience section prior to applyingAllow 1-5 working days for a response to any job enquiryYour application is subject to our privacy policy, found here: https://www.thurnpartners.com/privacy-policy
Equity Sector Data Analyst, London employer: LinkedIn
Contact Detail:
LinkedIn 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 the current market dynamics and how they impact investment strategies will help you engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the investment management sector, especially those who work in data analysis or equity research. Attend industry events or webinars to connect with potential colleagues and learn more 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 and how you've used them to drive insights. Being able to articulate your experience with alternative data and its relevance to equity analysis will showcase your expertise.
We think you need these skills to ace Equity Sector Data Analyst, London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, programming skills in SQL and Python, and any industry-specific knowledge, especially in healthcare or energy sectors. Use keywords from the job description to align your CV with the role.
Craft a Compelling Cover Letter: Write a cover letter that showcases your intellectual curiosity and passion for data-driven approaches in equity research. Mention specific examples of how you've used data to generate insights or improve processes in previous roles.
Highlight Collaboration Skills: Since the role involves collaboration across teams, emphasise your ability to work effectively with others. Provide examples of past experiences where you successfully exchanged results and feedback with colleagues or stakeholders.
Showcase Analytical Experience: Detail your experience with KPI estimation, hypothesis testing, and data validation. Include any specific projects or studies you've conducted that demonstrate your analytical skills and ability to handle complex datasets.
How to prepare for a job interview at LinkedIn
✨Showcase Your Analytical Skills
Prepare to discuss specific examples of how you've used data analysis in previous roles. Highlight your experience with SQL and Python, and be ready to explain complex datasets you've worked with, especially in the healthcare or energy sectors.
✨Understand the Company’s Approach
Familiarise yourself with the firm's investment strategies and how they leverage data. Be prepared to discuss how you can contribute to their data-driven approach and collaborate effectively with PMs and traders.
✨Demonstrate Collaboration Skills
Since the role involves working across teams, think of examples where you've successfully collaborated on projects. Emphasise your ability to exchange feedback and results, and how you can build custom tools that enhance team performance.
✨Prepare for Technical Questions
Expect technical questions related to KPI estimation, hypothesis testing, and data validation. Brush up on these concepts and be ready to discuss how you've applied them in your past work, particularly in relation to equity fundamental research.
