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- Tasks: Develop predictive models and deliver actionable market insights in trading analytics.
- Company: Join a leading oil and gas trading business in Greater London.
- Benefits: Competitive salary and opportunities for professional growth.
- Other info: Challenging role with excellent career advancement potential.
- Why this job: Make an impact in the financial services sector with your analytical skills.
- Qualifications: Strong quantitative skills, Python proficiency, and SQL Server DBA experience.
The predicted salary is between 60000 - 80000 £ per year.
Radley James is seeking an ambitious Trading Analyst in Greater London. You will develop predictive models, improve database performance, and deliver actionable market insights.
The role requires:
- Strong quantitative skills
- Proficiency in Python
- DBA experience with SQL Server
Familiarity with data visualization tools and experience in financial services are preferred. This position offers a challenging opportunity within a leading oil and gas trading business.
Trading Analytics Engineer employer: Radley James
Contact Detail:
Radley James Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Trading Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to professionals in the trading and analytics space on LinkedIn. Join relevant groups and engage in discussions to get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and data visualisations. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with Python, SQL Server, and any data visualisation tools you've used. Practice common interview questions related to trading analytics.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to show your enthusiasm for joining our team.
We think you need these skills to ace Trading Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your quantitative skills and experience with Python and SQL Server. We want to see how your background aligns with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about trading analytics and how your skills can contribute to our team. Keep it concise but impactful – we love a good story!
Showcase Your Data Visualisation Skills: If you’ve got experience with data visualisation tools, make sure to mention it! We’re keen on candidates who can turn complex data into actionable insights, so share examples of how you’ve done this in the past.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Radley James
✨Know Your Numbers
As a Trading Analytics Engineer, you'll need to showcase your strong quantitative skills. Brush up on key financial metrics and be ready to discuss how you've used data to drive decisions in previous roles.
✨Python Proficiency is Key
Make sure you can demonstrate your Python skills during the interview. Prepare to talk about specific projects where you've used Python for predictive modelling or data analysis, and be ready to solve a coding challenge if asked.
✨SQL Server Savvy
Since DBA experience with SQL Server is crucial, be prepared to discuss your experience with database performance improvements. Have examples ready that illustrate how you've optimised queries or managed large datasets effectively.
✨Visualise Your Insights
Familiarity with data visualisation tools is a plus, so think about how you've presented complex data in an understandable way. Bring along examples of dashboards or reports you've created that turned data into actionable insights.