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For Companies At a Glance
- Tasks: Design ML workflows and build predictive models using financial datasets.
- Company: Join a top-tier hedge fund at the forefront of financial technology.
- Benefits: Enjoy competitive pay, flexible working hours, and opportunities for growth.
- Why this job: Make a real impact on trading strategies while working with cutting-edge technology.
- Qualifications: MSc or PhD in a quantitative field; strong ML, NLP, and Python skills required.
- Other info: Experience with PyTorch, TensorFlow, or XGBoost is a plus.
The predicted salary is between 48000 - 84000 £ per year.
Top-tier hedge fund is looking for an ML Researcher with strong NLP and modeling experience. You will work with massive financial datasets to build predictive models that directly impact trading strategies.
What You'll Do:
- Design ML workflows from data ingestion to live deployment
- Build and optimize data pipelines and benchmarking frameworks
- Apply NLP and statistical methods to market data
What They're Looking For:
- Strong background in machine learning, NLP, and statistics
- Proficient in Python and data wrangling
- Experience with PyTorch, TensorFlow, or XGBoost (a plus)
- MSc or PhD in a quantitative field
Machine Learning Researcher employer: Alexander Chapman
Contact Detail:
Alexander Chapman Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Researcher
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and NLP, especially as they relate to financial datasets. This will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with the ML and finance communities online. Join forums, attend webinars, or participate in relevant discussions on platforms like LinkedIn. Networking can often lead to opportunities and insights that are not publicly advertised.
✨Tip Number 3
Consider working on personal projects that involve building predictive models using financial data. Showcase these projects on platforms like GitHub to demonstrate your practical skills and understanding of the subject matter.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges related to Python and machine learning algorithms. Websites like LeetCode or HackerRank can be great resources to sharpen your skills before the interview.
We think you need these skills to ace Machine Learning Researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning, NLP, and statistics. Include specific projects or roles where you've applied these skills, especially if they relate to financial datasets.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about machine learning and how your background aligns with the hedge fund's goals. Mention any relevant experience with Python, PyTorch, TensorFlow, or XGBoost.
Showcase Your Projects: If you have any personal or academic projects that demonstrate your ability to design ML workflows or apply NLP methods, include them in your application. Provide links to GitHub repositories or publications if possible.
Highlight Your Education: Clearly state your MSc or PhD qualifications in a quantitative field. If you have completed any relevant coursework or research, be sure to mention it as it can strengthen your application.
How to prepare for a job interview at Alexander Chapman
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, particularly in NLP and statistical methods. Highlight specific projects where you've applied these skills, especially if they relate to financial datasets.
✨Demonstrate Your Problem-Solving Ability
Expect questions that assess your analytical thinking and problem-solving skills. Be ready to walk through your thought process on how you would design ML workflows or optimise data pipelines.
✨Familiarise Yourself with Relevant Tools
Make sure you know the ins and outs of Python, as well as any libraries like PyTorch, TensorFlow, or XGBoost. If you have experience with these tools, be ready to discuss how you've used them in past projects.
✨Prepare for Technical Questions
You might face technical questions or even coding challenges during the interview. Brush up on your coding skills and be ready to demonstrate your knowledge of data wrangling and model deployment.
