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- Tasks: Collaborate with teams to design data pipelines and improve workflows for investment strategies.
- Company: Leading global investment firm focused on quantitative and systematic strategies.
- Benefits: Competitive salary, innovative culture, and opportunities for professional growth.
- Why this job: Join a high-impact team and make a difference in investment decision-making with data.
- Qualifications: 3+ years in data science or engineering, strong Python skills, and interest in finance.
- Other info: Fast-paced environment with excellent career advancement opportunities.
The predicted salary is between 36000 - 60000 £ per year.
A leading global investment firm specialising in quantitative and systematic strategies is seeking a talented Data Scientist (with good data engineering skills) to join a high-impact team at the intersection of research, trading, and engineering. The firm operates across all major liquid asset classes and is driven by a scientific, data-first approach to investing. With a strong culture of innovation and collaboration, the team continuously explores new ways to harness data for alpha generation. About the Role This is a unique opportunity to work in a cross-functional environment where data is central to investment decision-making. You\’ll be part of a specialist group that partners closely with trading and research teams to ensure data is efficiently sourced, transformed, and deployed across the investment process. Key Responsibilities Collaborate with trading and research teams to design and implement robust data pipelines tailored to investment strategies. Work with data engineers to onboard and integrate new datasets, ensuring they are production-ready and aligned with business needs. Develop and prototype tools to extract, clean, and aggregate data from diverse sources and formats. Lead the end-to-end onboarding of new datasets, from discovery through to deployment. Continuously improve data workflows by identifying bottlenecks and implementing scalable solutions. Experiment with novel data acquisition and transformation techniques to expand the firm\’s data capabilities. Ideal Candidate Profile 3+ years of experience in a data science or data engineering role, ideally within a quantitative or financial setting. Advanced degree (Master\’s or PhD) in a quantitative field such as Mathematics, Physics, Computer Science, or Engineering. Strong Python programming skills, particularly with data-centric libraries like Pandas and NumPy. Demonstrated interest in financial markets and the application of data to investment research. Experience working with both traditional and alternative financial datasets. Excellent communication skills and the ability to collaborate effectively with technical and non-technical stakeholders. Comfortable working in a fast-paced, high-performance environment. If you feel the above fits with your expeirence and are ready to join a fast paced and high performing fund, apply today!
Data Scientist (Commodities) | Top Systematic Fund employer: Selby Jennings
Contact Detail:
Selby Jennings Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Commodities) | Top Systematic Fund
✨Tip Number 1
Network like a pro! Reach out to current employees at the firm on LinkedIn or through mutual connections. A friendly chat can give us insights into the company culture and maybe even a referral!
✨Tip Number 2
Prepare for the technical interview by brushing up on your Python skills and data engineering concepts. We recommend working on real-world projects that showcase your ability to handle data pipelines and analytics.
✨Tip Number 3
Showcase your passion for financial markets! During interviews, share examples of how you've applied data science in finance. This will help us see your enthusiasm and fit for the role.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Data Scientist (Commodities) | Top Systematic Fund
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Scientist role. Highlight your data engineering skills and any relevant projects you've worked on, especially those related to financial datasets.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about data science in the financial sector. Share specific examples of how you've used data to drive decisions or improve processes, and show us your enthusiasm for joining our innovative team.
Showcase Your Technical Skills: Since we're looking for strong Python programming skills, make sure to mention your experience with libraries like Pandas and NumPy. If you have any projects or GitHub repositories, link them to give us a better idea of your capabilities.
Apply Through Our Website: We encourage you to apply directly through our website. This way, your application will be processed more efficiently, and you'll be one step closer to joining our high-impact team!
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 data pipelines and be ready to discuss specific examples of how you've sourced, transformed, and deployed data in previous roles.
✨Showcase Your Python Skills
Since strong Python programming skills are a must, prepare to demonstrate your proficiency. Bring along examples of projects where you’ve used libraries like Pandas and NumPy to solve real-world problems, and be ready to explain your thought process.
✨Understand the Financial Landscape
Familiarise yourself with both traditional and alternative financial datasets. Be prepared to discuss how data influences investment decisions and share your insights on current market trends to show your genuine interest in the field.
✨Communicate Clearly and Collaboratively
As collaboration is key in this role, practice articulating your ideas clearly. Think about how you can effectively communicate complex data concepts to both technical and non-technical stakeholders, and be ready to provide examples of successful teamwork.
