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 focus on innovation.
- Benefits: Strong learning opportunities, collaborative environment, and exposure to cutting-edge AI projects.
- Other info: Ideal for those who thrive in collaborative and agile settings.
- Why this job: Make an impact in global markets while working with advanced analytics and risk models.
- Qualifications: Experience with Python, R, and large datasets; strong analytical skills required.
The predicted salary is between 60000 - 80000 £ 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: AI & Risk Analytics for Global Markets employer: James Adams
Join a globally recognised research and analytics organisation that champions innovation and collaboration in the field of AI and risk analytics. With a strong focus on employee growth, you will have access to cutting-edge projects and complex datasets, all within a supportive environment that encourages continuous learning and development. This role not only offers the chance to work alongside experts in various fields but also provides a unique opportunity to make a significant impact in the financial services sector.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist: AI & Risk Analytics for Global Markets
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and NLP. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Data Scientist: AI & Risk Analytics for Global Markets
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with large datasets, machine learning, and any relevant tools like Python or R. We want to see how you can contribute to our exciting projects!
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 your background aligns with our needs. Don’t forget to mention your collaborative spirit and problem-solving skills – we love team players!
Showcase Your Projects:If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We’re interested in seeing how you’ve applied your skills in real-world scenarios, especially in machine learning and NLP.
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 don’t miss out on any important updates. Plus, it shows you’re keen to join our team at StudySmarter!
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 well as SQL and database technologies. Being able to discuss your technical skills confidently will impress the interviewers.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data challenges in the past. Think about specific projects where you developed predictive models or analysed large datasets. This will demonstrate your analytical abilities and how you can contribute to their team.
✨Communicate Clearly
Practice explaining technical concepts in simple terms. You’ll need to convey your ideas to non-technical stakeholders, so being able to break down complex information is key. Consider doing mock interviews with friends to refine this skill.
✨Familiarise Yourself with Their Tools
If you have experience with AWS, Databricks, or Snowflake, be ready to discuss it. Even if you don’t, showing a willingness to learn about tools like MLFlow or Weights & Biases can set you apart. It shows you're proactive and eager to grow in the role.
