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- Tasks: Join a top-tier team to develop investment strategies using ML and NLP.
- Company: Be part of a leading investment management firm focused on data-driven research.
- Benefits: Enjoy a competitive salary, hybrid working, and opportunities for academic publishing.
- Why this job: Blend academic research with real-world impact in a collaborative environment.
- Qualifications: PhD in a quantitative field, strong Python skills, and interest in financial data.
- Other info: Ideal for recent graduates eager to make a difference in systematic investing.
The predicted salary is between 90000 - 120000 £ per year.
Join a world-class team using ML and NLP to shape methodical research strategies. High-impact role for a recent PhD graduate in a cutting-edge investment research team. Work with large-scale data and the latest ML and NLP frameworks. Combine academic research with real-world application in a collaborative setting.
Work at the frontier of data-led investment strategies, using the latest techniques in machine learning, natural language processing, and quantitative modelling. This position blends academic insight with practical application in a collaborative and intellectually stimulating environment. You’ll have the opportunity to shape systematic strategies across asset classes, while contributing meaningfully to both internal and external research publications.
What you’ll be doing:
- As a Senior Data Scientist, you’ll be part of a high-performing research team dedicated to developing and refining systematic investment models.
- You’ll work hands-on with large and alternative datasets, employing tools like Python, PyTorch, and modern machine learning frameworks to design, test and evolve predictive signals and strategies.
- Your role will be an exciting mix of theoretical research and practical implementation, giving you the opportunity to see your ideas translated into real-world investment decisions.
- You’ll collaborate with economists, technologists and fellow data scientists in a highly supportive, cross-disciplinary environment.
- Not only will you be encouraged to publish and continue your academic interests, but you’ll also be making a tangible impact within a team at the forefront of systematic investing.
- This is a role that values curiosity, innovation, and is bridging academic with financial insight.
What experience you’ll need to apply:
- A PhD in a quantitative discipline (e.g. mathematics, statistics, computer science, engineering, or physics), completed within the last two years.
- Strong programming skills in Python or a similar language.
- Experience with machine learning, NLP, or statistical modelling.
- A genuine interest in applying academic research to real-world problems.
- Experience peer reviewing papers, journals etc.
- Excellent communication and collaboration skills.
- Exposure to financial data or investment research is preferable.
What you’ll get in return:
A salary of between £90,000 - £120,000 depending on experience. They also offer hybrid working, meaning two days from home and three in the London office.
What’s next?
Apply with your updated CV and we’ll be in touch to arrange a call and discuss the role in more detail if it’s a good fit!
Senior Data Scientist - Research employer: ADLIB Recruitment | B CorpTM
Contact Detail:
ADLIB Recruitment | B CorpTM Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist - Research
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and natural language processing. Being able to discuss recent advancements or breakthroughs in these areas during your interview will demonstrate your passion and commitment to the field.
✨Tip Number 2
Prepare to showcase your hands-on experience with large datasets. Think of specific projects where you applied Python or similar programming languages to solve complex problems, as this will highlight your practical skills and ability to contribute to our team.
✨Tip Number 3
Brush up on your communication skills, especially in explaining complex concepts to non-technical stakeholders. This role requires collaboration with economists and technologists, so being able to convey your ideas clearly will set you apart.
✨Tip Number 4
Engage with the investment research community by reading relevant papers and publications. This will not only enhance your knowledge but also provide you with talking points that can impress during interviews, showing your genuine interest in applying academic research to real-world scenarios.
We think you need these skills to ace Senior Data Scientist - Research
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your PhD and relevant experience in quantitative disciplines. Emphasise your programming skills in Python and any experience with machine learning or NLP, as these are crucial for the role.
Craft a Compelling Cover Letter: In your cover letter, express your genuine interest in applying academic research to real-world problems. Mention specific projects or experiences that demonstrate your ability to work with large datasets and your collaborative skills.
Showcase Relevant Projects: Include details of any relevant projects or research you have conducted, particularly those involving predictive modelling or statistical analysis. Highlight your contributions and the impact of your work on investment strategies or similar fields.
Prepare for Technical Questions: Be ready to discuss your technical skills and experiences in detail. Prepare to explain your approach to machine learning and NLP projects, and how you have applied theoretical concepts in practical settings.
How to prepare for a job interview at ADLIB Recruitment | B CorpTM
✨Showcase Your Research Skills
Be prepared to discuss your PhD research in detail. Highlight how your academic work relates to real-world applications, especially in investment strategies. This will demonstrate your ability to bridge theory and practice.
✨Demonstrate Technical Proficiency
Make sure you can confidently talk about your programming skills, particularly in Python and any machine learning frameworks you've used. Be ready to provide examples of projects where you've applied these skills effectively.
✨Prepare for Collaborative Scenarios
Since the role involves working with economists and technologists, think of examples from your past experiences where you successfully collaborated in a team. Emphasise your communication skills and how you contribute to a supportive environment.
✨Express Your Passion for Financial Data
Even if your background is primarily academic, show genuine interest in financial data and investment research. Discuss any relevant experiences or insights you've gained that connect your research to the finance sector.
