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- Tasks: Transform raw data into actionable insights for investment strategies.
- Company: Leading global investment firm at the forefront of finance.
- Benefits: Competitive salary, dynamic work environment, and opportunities for growth.
- Why this job: Join a specialist team and make a real impact in finance with data.
- Qualifications: 3+ years in data science, strong Python skills, and a passion for finance.
- Other info: Fast-paced environment with collaboration across diverse teams.
The predicted salary is between 43200 - 72000 £ per year.
A leading global investment firm is seeking a Data Scientist to join a specialist team at the intersection of research, trading, and engineering. This group plays a critical role in leveraging data to power systematic and quantamental strategies across multiple asset classes.
About the Role
You will work closely with quantitative researchers, traders, and engineers to transform raw data into actionable insights. The role involves designing and onboarding new datasets, building features and signals for backtesting, and proving the value of data for investment strategies. Expect to work with large-scale alternative datasets and collaborate across equities and commodities teams in a fast-paced, high-performance environment.
Key Responsibilities
- Partner with researchers and traders to design datasets that drive systematic strategies and inform discretionary decisions.
- Prototype and develop tools to extract, clean, and aggregate data from diverse sources and formats.
- Build features and signals for backtesting to validate dataset potential for alpha generation.
- Manage the end-to-end onboarding of new datasets, ensuring scalability and robustness.
- Collaborate with engineers to optimize workflows and automate data processes.
- Experiment with innovative data acquisition and transformation techniques to expand the firm’s data capabilities.
Ideal Candidate Profile
- 3+ years of experience in data science or data engineering, ideally within quantitative finance.
- Advanced degree in a quantitative discipline (Mathematics, Physics, Computer Science, Engineering).
- Strong Python programming skills (experience with Pandas, NumPy; familiarity with Polars a plus).
- Proven ability to work with large-scale alternative and traditional financial datasets.
- Interest in financial markets and applying data to investment research.
- Excellent communication skills and ability to collaborate with technical and non-technical stakeholders.
- Comfortable working in a high-performance, fast-paced environment.
If you feel this role is a good match - apply today!
Data Scientist | Multi-Strat Hedge Fund | London employer: Selby Jennings
Contact Detail:
Selby Jennings Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist | Multi-Strat Hedge Fund | London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at hedge funds or in data science roles. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those related to finance. This is your chance to demonstrate how you can turn raw data into actionable insights, just like the role requires.
✨Tip Number 3
Prepare for interviews by brushing up on your Python skills and understanding financial datasets. Be ready to discuss how you've tackled similar challenges in the past and how you can contribute to systematic strategies.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace Data Scientist | Multi-Strat Hedge Fund | London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience in data science and any relevant projects that showcase your skills in Python, data cleaning, and feature engineering. We want to see how your background aligns with the responsibilities outlined in the job description.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our team. Mention specific experiences that relate to working with large datasets and collaborating with researchers and traders, as this will resonate with us.
Showcase Your Technical Skills: Don’t forget to highlight your technical skills, especially in Python and any libraries like Pandas or NumPy. If you have experience with alternative datasets or financial markets, make sure to include that too. We love seeing candidates who can demonstrate their technical prowess!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for the role. Plus, it’s super easy to do – just follow the prompts and submit your materials!
How to prepare for a job interview at Selby Jennings
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets relevant to the role. Brush up on your experience with large-scale alternative datasets and be ready to discuss how you've transformed raw data into actionable insights in previous roles.
✨Showcase Your Technical Skills
Be prepared to demonstrate your Python programming skills, especially with libraries like Pandas and NumPy. You might even want to bring along a project or two that highlights your ability to prototype tools for data extraction and cleaning.
✨Understand the Financial Landscape
Familiarise yourself with the financial markets and how data influences investment strategies. Be ready to discuss your interest in quantitative finance and how you’ve applied data science principles to real-world trading scenarios.
✨Communicate Effectively
Since collaboration is key in this role, practice explaining complex technical concepts in simple terms. Think about examples where you’ve successfully worked with both technical and non-technical stakeholders to drive projects forward.
