At a Glance
- Tasks: Develop and deploy machine learning models to tackle large-scale data challenges.
- Company: Globally recognised research and analytics organisation with a collaborative culture.
- Benefits: Competitive salary, hybrid working model, and strong learning opportunities.
- Other info: Dynamic environment with exposure to advanced technologies and career growth.
- Why this job: Work on cutting-edge AI projects and make a real impact in global markets.
- Qualifications: Experience with Python, R, and large datasets; strong analytical skills required.
The predicted salary is between 90000 - 90000 £ per year.
Location: London (Hybrid)
Type: Permanent
Salary: Up to £90,000
The Opportunity
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
- Hybrid working model based in London
Data Scientist employer: James Adams
Join a globally recognised research and analytics organisation that champions innovation and collaboration in the heart of London. As a Data Scientist, you will thrive in a dynamic work culture that prioritises employee growth through exposure to cutting-edge AI projects and complex datasets, all while enjoying the flexibility of a hybrid working model. With strong learning and development opportunities, this role offers a meaningful chance to contribute to impactful analytics solutions within a supportive team environment.
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 on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and NLP. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms – it’s key for communicating with non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented Data Scientists, and applying directly can give you an edge in the process.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Scientist role. Highlight your experience with machine learning, NLP, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our team. Be sure to mention any specific experiences that relate to the job description.
Showcase Your Technical Skills:Don’t forget to showcase your technical skills in Python, R, and SQL. We love seeing examples of your work, so if you have any projects or GitHub repositories, include them in your application!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you're keen on joining our team!
How to prepare for a job interview at James Adams
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets you’ll be working with. Brush up on your experience with large datasets and quantitative analysis, as you might be asked to discuss specific projects or challenges you've faced. Be ready to explain how you approached data cleaning, analysis, and the insights you derived.
✨Showcase Your Technical Skills
Prepare to demonstrate your proficiency in Python, R, and relevant ML libraries. You might be asked to solve a problem on the spot or discuss your previous work with machine learning and NLP models. Bring examples of your code or projects that highlight your technical abilities and understanding of software engineering best practices.
✨Communicate Clearly with Non-Technical Stakeholders
Since the role involves explaining complex concepts to non-technical stakeholders, practice how you would break down your technical outputs. Think of ways to simplify your explanations without losing the essence of your work. This will show your ability to bridge the gap between technical and commercial aspects.
✨Collaborate and Adapt
Highlight your experience working in collaborative or agile environments. Be prepared to discuss how you’ve worked with cross-functional teams, including economists and analysts. Share examples of how you adapted your approach based on feedback or changing project requirements, showcasing your flexibility and teamwork skills.
