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- Tasks: Lead data analysis and predictive modelling to shape investment strategies.
- Company: Dynamic hedge fund in Greater London focused on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Make a real impact on investment decisions using cutting-edge data science techniques.
- Qualifications: Strong Python and SQL skills, with experience in advanced analytics or ML.
- Other info: Join a collaborative team and enhance your career in finance analytics.
The predicted salary is between 36000 - 60000 £ per year.
A hedge fund based in Greater London is seeking a Data Scientist to lead the development of its data-driven investment strategy. This role involves data analysis, predictive modeling, and collaboration with engineers to forecast critical company KPIs.
The ideal candidate will have strong Python and SQL skills, along with experience in advanced analytics or ML in financial contexts. This position provides an excellent opportunity to influence investment decisions and advance the firm's data science capabilities.
Data Scientist – Real-Time Finance Analytics & ML in London employer: Jobster
Contact Detail:
Jobster Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – Real-Time Finance Analytics & ML in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the finance and data science sectors on LinkedIn. A friendly message can go a long way, and you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python and SQL projects, especially those related to finance. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your predictive modelling techniques and financial analytics knowledge. We recommend practising common interview questions and even doing mock interviews with friends or mentors.
✨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 are proactive about their job search!
We think you need these skills to ace Data Scientist – Real-Time Finance Analytics & ML in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your Python and SQL skills in your application. We want to see how you've used these tools in real-world scenarios, especially in finance or analytics.
Tailor Your Experience: When writing your application, focus on your experience with predictive modelling and advanced analytics. We’re looking for specific examples that demonstrate your ability to influence investment strategies.
Collaborate Like a Pro: Since this role involves working closely with engineers, mention any past experiences where you collaborated on projects. We love seeing teamwork in action!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and get the ball rolling on your journey with us.
How to prepare for a job interview at Jobster
✨Know Your Data Science Fundamentals
Brush up on your data science basics, especially in Python and SQL. Be prepared to discuss how you've used these skills in previous roles, particularly in financial contexts. This will show that you can hit the ground running.
✨Showcase Your Predictive Modelling Skills
Prepare to talk about specific projects where you've developed predictive models. Highlight the techniques you used and the impact they had on decision-making. Real-world examples will make your experience more relatable and impressive.
✨Understand the Hedge Fund Landscape
Familiarise yourself with the hedge fund industry and current trends in finance analytics. Being able to discuss how data science can influence investment strategies will demonstrate your genuine interest and understanding of the role.
✨Collaborate Like a Pro
Since this role involves working closely with engineers, be ready to discuss your collaboration experiences. Share examples of how you've successfully worked in teams to achieve common goals, especially in high-pressure environments.
