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- Tasks: Develop predictive models for finance and collaborate with diverse teams.
- Company: Leading financial tech company in London with a focus on innovation.
- Benefits: Flexible work model, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact in finance using cutting-edge machine learning techniques.
- Qualifications: Expertise in Python, deep learning, and experience in financial services.
- Other info: Dynamic environment with a strong emphasis on collaboration and innovation.
The predicted salary is between 36000 - 60000 £ per year.
A leading financial technology company in London is looking for a Machine Learning Specialist/Data Scientist to contribute to predictive modeling within alternative asset management. You will develop and implement models like recommendation systems and risk analysis while collaborating across teams.
Ideal candidates will possess expertise in Python, deep learning architectures, and have experience in financial services. The role supports a flexible work model, promoting both remote and in-office collaboration.
ML Specialist: Predictive Finance & Portfolio Insights in London employer: CAIS
Contact Detail:
CAIS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Specialist: Predictive Finance & Portfolio Insights in London
✨Tip Number 1
Network like a pro! Reach out to folks in the financial tech space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to finance. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your Python and deep learning knowledge. Be ready to discuss how you've applied these skills in real-world scenarios, particularly in finance.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are proactive and engaged. Plus, it makes it easier for us to keep track of your application.
We think you need these skills to ace ML Specialist: Predictive Finance & Portfolio Insights in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and deep learning architectures. We want to see how your skills align with predictive modelling in finance, so don’t hold back on showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about alternative asset management and how your background makes you the perfect fit for our team. Let us know what excites you about this role!
Showcase Collaboration Skills: Since we value teamwork, mention any past experiences where you’ve collaborated across teams. Highlighting your ability to work well with others will show us you’re a great fit for our flexible work model.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at CAIS
✨Know Your Models
Make sure you can discuss the predictive models you've worked on in detail. Be ready to explain your approach to developing recommendation systems and risk analysis models, as well as the specific algorithms and deep learning architectures you've used.
✨Brush Up on Financial Knowledge
Since this role is within alternative asset management, it’s crucial to have a solid understanding of financial concepts. Familiarise yourself with key terms and trends in the financial services sector to demonstrate your expertise and relevance to the company.
✨Showcase Your Python Skills
Prepare to discuss your experience with Python in depth. Bring examples of projects where you've implemented machine learning solutions, and be ready to solve coding challenges or answer technical questions related to Python during the interview.
✨Emphasise Collaboration
This role involves working across teams, so highlight your teamwork skills. Share examples of how you've successfully collaborated with others in previous roles, especially in cross-functional settings, to show that you're a great fit for their flexible work model.
