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- Tasks: Automate data-quality processes and build scalable machine-learning pipelines.
- Company: Leading financial services firm with a focus on innovation.
- Benefits: Salary up to £100k, hybrid work model, and career growth opportunities.
- Why this job: Join a dynamic team and make an impact in the finance sector with cutting-edge technology.
- Qualifications: 2+ years in a data-driven role, proficient in Python and SQL.
- Other info: Exciting opportunity in Central London with a collaborative work culture.
The predicted salary is between 60000 - 84000 £ per year.
A leading financial services firm is seeking a skilled Machine Learning Engineer to enhance their Data team. In this role, you will be responsible for automating data-quality processes and transforming existing models into scalable machine-learning pipelines.
The ideal candidate should have over 2 years of experience in a data-driven role with proficiency in Python and SQL. Knowledge of AWS and experience in the financial services sector will be beneficial.
This position offers a salary of up to £100k and a hybrid working model in Central London.
Hybrid ML Engineer - Finance | Python & AWS employer: Oliver Bernard
Contact Detail:
Oliver Bernard Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Hybrid ML Engineer - Finance | Python & AWS
✨Tip Number 1
Network like a pro! Reach out to people in the finance and tech 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 machine learning projects, especially those involving Python and AWS. 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 common ML concepts and financial services knowledge. Practice explaining your past projects and how they relate to the role. Confidence is key, so let your passion for data shine through!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might 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 Hybrid ML Engineer - Finance | Python & AWS
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Python, SQL, and any relevant AWS projects. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning in finance and how your background makes you a perfect fit for our team. Keep it engaging and personal.
Showcase Relevant Projects: If you've worked on any machine learning projects, especially in finance, make sure to mention them. We love seeing real-world applications of your skills, so include links or descriptions of your work!
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!
How to prepare for a job interview at Oliver Bernard
✨Know Your Tech Inside Out
Make sure you brush up on your Python and SQL skills before the interview. Be ready to discuss specific projects where you've used these languages, especially in a data-driven context. If you've worked with AWS, prepare to explain how you've leveraged it in your previous roles.
✨Showcase Your Financial Acumen
Since this role is in the financial services sector, it's crucial to demonstrate your understanding of finance-related data challenges. Think about how machine learning can solve problems in this field and be prepared to share examples from your experience.
✨Prepare for Technical Questions
Expect technical questions that test your knowledge of machine learning concepts and data quality processes. Practice explaining complex ideas in simple terms, as you may need to communicate these concepts to non-technical stakeholders.
✨Ask Insightful Questions
At the end of the interview, have a few thoughtful questions ready. Inquire about the team’s current projects or the company’s approach to data quality automation. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
