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- Tasks: Join a dynamic team to analyse data and develop machine learning models.
- Company: Leading financial services firm in Central London with a focus on innovation.
- Benefits: Competitive salary up to £100k, hybrid working, and comprehensive benefits package.
- Why this job: Make an impact in finance using your skills in Python and machine learning.
- Qualifications: Over 2 years of experience in data science and a quantitative degree.
- Other info: Exciting opportunity for career growth in a collaborative environment.
The predicted salary is between 60000 - 84000 £ per year.
A leading financial services firm is seeking a Data Scientist with a strong background in machine learning, Python, and SQL to join their expanding data team in Central London. The ideal candidate will have over 2 years of experience and a quantitative degree (MSc/PhD preferred). This role offers a salary up to £100k based on skills and experience, along with a benefits package and hybrid working model, requiring three days in the London office.
Finance ML Data Scientist Hybrid London (Python, AWS) employer: Oliver Bernard
Contact Detail:
Oliver Bernard Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Finance ML Data Scientist Hybrid London (Python, AWS)
✨Tip Number 1
Network like a pro! Reach out to professionals in the finance and data science sectors on LinkedIn. Join relevant groups and engage in discussions to get your name out there.
✨Tip Number 2
Prepare for those interviews! Brush up on your machine learning concepts, Python skills, and SQL queries. Practise common interview questions and maybe even do some mock interviews with friends.
✨Tip Number 3
Showcase your projects! If you've worked on any cool data science projects, make sure to highlight them during interviews. Having tangible examples can really set you apart from other candidates.
✨Tip Number 4
Apply through our website! We’ve got loads of opportunities that might just be perfect for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Finance ML Data Scientist Hybrid London (Python, AWS)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning, Python, and SQL. We want to see how your skills align 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 data science and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Quantitative Skills: Since a quantitative degree is preferred, make sure to highlight any relevant coursework or research in your application. We’re looking for candidates who can demonstrate their analytical prowess, so don’t hold back!
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 Oliver Bernard
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, SQL, and any machine learning frameworks relevant to the role. Brush up on your past projects and be ready to discuss how you applied these technologies to solve real-world problems.
✨Showcase Your Quantitative Skills
Since a quantitative degree is preferred, be prepared to talk about your academic background and how it relates to the job. Highlight any specific projects or research that demonstrate your analytical abilities and understanding of financial data.
✨Understand the Company’s Landscape
Research the financial services firm thoroughly. Understand their products, market position, and recent news. This will not only help you tailor your answers but also show your genuine interest in the company.
✨Prepare for Behavioural Questions
Expect questions about teamwork, problem-solving, and handling challenges. Use the STAR method (Situation, Task, Action, Result) to structure your responses, ensuring you provide clear examples from your experience.
