At a Glance
- Tasks: Develop and deploy machine learning and NLP models to tackle large-scale data challenges.
- Company: Globally recognised research and analytics organisation with a collaborative culture.
- Benefits: Strong learning opportunities, exposure to complex datasets, and a dynamic work environment.
- Other info: Ideal for those who thrive in collaborative and agile environments.
- Why this job: Join a team driving innovation in AI and analytics while making a real-world impact.
- Qualifications: Experience with Python, R, and large datasets; strong analytical and problem-solving skills.
The predicted salary is between 50000 - 70000 £ per year.
We’re working with a globally recognised research and analytics organisation that is looking to hire a Data Scientist to support the development of advanced risk analytics and machine learning solutions. This is an exciting opportunity to work on large-scale data challenges, helping deliver insights and predictive models used by major organisations across financial services, professional services, and investment markets.
The Role
You’ll work closely with economists, analysts, developers, and data scientists to design, build, and deploy quantitative models that drive analytics and decision-making. The role combines machine learning, NLP, data engineering, and research, making it ideal for someone who enjoys working across both technical delivery and commercial problem solving.
Key Responsibilities
- Develop and deploy machine learning and NLP models
- Build and maintain scalable data and ML pipelines
- Analyse structured and unstructured datasets to generate actionable insights
- Design and optimise predictive and risk models
- Collaborate with analysts, economists, and developers across multiple teams
- Explain technical outputs and methodologies to non-technical stakeholders
- Support experimentation, model tracking, and continuous improvement initiatives
- Contribute to the development of AI and analytics capabilities
What We’re Looking For
- Strong experience working with large datasets and quantitative analysis
- Commercial experience with Python, R, and relevant ML libraries
- Experience developing and monitoring machine learning or NLP models
- Understanding of software engineering best practices including testing and version control
- Knowledge of SQL and database technologies
- Strong analytical and problem-solving skills
- Ability to communicate technical concepts clearly to a range of stakeholders
- Experience working in collaborative or agile environments
Nice to Have
- Exposure to AWS, Databricks, or Snowflake
- Experience with NLP, generative AI, or computational social science methods
- Familiarity with MLFlow, Weights & Biases, or similar tools
- Knowledge of time series forecasting, simulation modelling, or stress testing
Why Apply
- Opportunity to work on cutting‑edge AI and analytics projects
- Exposure to complex global market and risk datasets
- Collaborative and research‑driven environment
- Strong learning and development opportunities
Data Scientist employer: James Adams
Join a globally recognised research and analytics organisation that champions innovation and collaboration. As a Data Scientist, you'll engage in cutting-edge AI projects within a supportive environment that fosters professional growth and development. With access to complex global datasets and the opportunity to work alongside experts in various fields, this role offers a unique chance to make a meaningful impact in the financial services and investment markets.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. We can’t stress enough how personal connections can open doors to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and NLP. We recommend using platforms like GitHub to share your code and demonstrate your problem-solving abilities.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you’ll need to communicate effectively with non-technical stakeholders. We suggest doing mock interviews with friends or mentors.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search. Let’s get you that Data Scientist role!
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with large datasets and machine learning. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or tools you've used!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about the Data Scientist role and how your background makes you a perfect fit. We love seeing genuine enthusiasm for the work we do.
Showcase Your Technical Skills:Don’t forget to mention your experience with Python, R, and any ML libraries. We’re looking for someone who can hit the ground running, so highlight any relevant projects or achievements that demonstrate your technical prowess.
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 this exciting opportunity. Plus, it’s super easy!
How to prepare for a job interview at James Adams
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, R, and the relevant ML libraries. Brush up on your knowledge of machine learning and NLP models, as you’ll likely be asked to discuss your experience with these technologies during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex data challenges. Think about how you’ve used quantitative analysis to generate insights or improve processes, and be ready to explain your thought process clearly.
✨Communicate Like a Pro
Since you'll need to explain technical concepts to non-technical stakeholders, practice simplifying your explanations. Use analogies or real-world examples to make your points relatable and ensure everyone understands your contributions.
✨Collaborate and Connect
Highlight your experience working in collaborative environments. Be prepared to discuss how you’ve worked with economists, analysts, and developers in the past, and emphasise your ability to thrive in team settings.
