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- Tasks: Design ML workflows and build predictive models using financial datasets.
- Company: Join a top-tier hedge fund making waves in the finance world.
- Benefits: Enjoy competitive pay, flexible work options, and a dynamic team environment.
- Why this job: Make a real impact on trading strategies while working with cutting-edge technology.
- Qualifications: PhD or MSc in a quantitative field with strong ML and NLP skills required.
- Other info: Experience with PyTorch or TensorFlow is a bonus!
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'll 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
ML Researcher (PhD) employer: Alexander Chapman
Contact Detail:
Alexander Chapman Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Researcher (PhD)
✨Tip Number 1
Network with professionals in the finance and machine learning sectors. Attend industry conferences, webinars, or meetups to connect with people who work at hedge funds or in similar roles. This can help you gain insights into the company culture and potentially get a referral.
✨Tip Number 2
Showcase your projects related to NLP and machine learning on platforms like GitHub. Having a strong portfolio that demonstrates your ability to handle financial datasets and build predictive models will make you stand out to recruiters.
✨Tip Number 3
Stay updated on the latest trends and advancements in machine learning and NLP, especially as they relate to finance. Being knowledgeable about current research and methodologies can give you an edge during interviews.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and ML problem-solving scenarios. Familiarise yourself with common algorithms and frameworks like PyTorch and TensorFlow, as these are often discussed in interviews for ML roles.
We think you need these skills to ace ML Researcher (PhD)
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 research that demonstrate your proficiency in Python and any relevant frameworks like PyTorch or TensorFlow.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about applying machine learning to financial datasets. Mention any relevant experience with predictive modelling and how it aligns with the company's goals.
Showcase Your Technical Skills: If you have worked on projects involving data pipelines or ML workflows, be sure to detail these experiences. Highlight any specific tools or methodologies you used, especially those related to data ingestion and live deployment.
Prepare for Technical Questions: Anticipate technical questions related to machine learning and NLP during the interview process. Brush up on your knowledge of statistical methods and be ready to discuss how you've applied them in real-world scenarios.
How to prepare for a job interview at Alexander Chapman
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, NLP, and statistical methods in detail. Highlight specific projects where you've applied these skills, especially those involving large datasets or predictive modelling.
✨Demonstrate Your Problem-Solving Ability
Expect to face technical challenges during the interview. Practice explaining your thought process when tackling complex problems, particularly in designing ML workflows or optimising data pipelines.
✨Familiarise Yourself with Financial Data
Since the role involves working with financial datasets, it’s beneficial to understand how market data is structured and used. Brush up on relevant financial concepts that could impact trading strategies.
✨Prepare Questions About the Role
Show your interest by preparing insightful questions about the company's approach to machine learning and how they integrate it into their trading strategies. This demonstrates your enthusiasm and helps you gauge if the company is the right fit for you.
