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- Tasks: Develop predictive models and enhance platform intelligence using machine learning.
- Company: Dynamic financial technology company based in London with a hybrid working model.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make an impact in the finance sector with your skills.
- Qualifications: Proven expertise in Python and machine learning, plus experience in financial services.
- Other info: Collaborative environment with a focus on innovation and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
A financial technology company in London is seeking a Machine Learning Specialist / Data Scientist to develop predictive models and enhance their platform's intelligence. The ideal candidate will have proven expertise in Python and machine learning techniques, alongside experience in the financial services industry.
This role involves collaborating with various teams, developing robust models, and deploying them in a cloud environment. The company promotes a hybrid working model, emphasizing both remote and in-person collaboration.
Remote ML Scientist - Finance & Portfolio Forecasting employer: CAIS
Contact Detail:
CAIS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Remote ML Scientist - Finance & Portfolio Forecasting
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and tech sectors on LinkedIn. Join relevant groups and participate in discussions to get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to finance. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and financial forecasting techniques. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Remote ML Scientist - Finance & Portfolio Forecasting
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your expertise in Python and machine learning techniques right from the get-go. We want to see how your skills align with our needs, so don’t hold back!
Tailor Your Application: Customise your CV and cover letter to reflect your experience in the financial services industry. We love seeing how your background fits into what we do, so make it relevant!
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary to showcase your expertise.
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 without any hiccups!
How to prepare for a job interview at CAIS
✨Know Your ML Stuff
Brush up on your machine learning techniques and be ready to discuss specific models you've developed. Make sure you can explain your thought process and the impact of your work in the financial services industry.
✨Python Proficiency is Key
Since Python is a must-have for this role, be prepared to showcase your coding skills. You might be asked to solve a problem on the spot, so practice writing clean, efficient code that demonstrates your expertise.
✨Collaboration is Crucial
This role involves working with various teams, so highlight your experience in collaborative projects. Share examples of how you've successfully worked with others to develop and deploy models, especially in a cloud environment.
✨Understand the Company’s Vision
Research the company’s platform and its approach to finance and portfolio forecasting. Being able to discuss how your skills align with their goals will show that you're genuinely interested and invested in the role.
