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- Tasks: Develop impactful models and analytics to transform higher education globally.
- Company: Join QS, a leader in higher education services with a vibrant culture.
- Benefits: Enjoy competitive salary, flexible work, wellness initiatives, and professional growth opportunities.
- Why this job: Make a real difference in education while enhancing your data science skills.
- Qualifications: Experience in machine learning, Python, SQL, and a degree in a quantitative field.
- Other info: Be part of a diverse team driving change across the global education landscape.
The predicted salary is between 30000 - 50000 £ per year.
Location: UK, London
Applicants must have the existing right to work in the UK. This role is not eligible for visa sponsorship.
Job type: Full time, Permanent – Hybrid. This position offers a hybrid work model, allowing flexibility between working from home and our office. Typically, employees are expected to work 2 days in the office per week.
At QS, we believe that work should empower you. That’s why we foster a flexible working environment that encourages every employee to own their career whilst flourishing personally and professionally. Our company values underpin everything we do – we collaborate, respect and support each other.
It’s our mission to empower motivated people around the world to fulfil their potential through higher education, ensuring that everyone has access to opportunities that change lives. Our diversity makes us stronger. By sharing our experiences, we learn from one another and achieve more together, driving progress across the sector.
As a Data Scientist, you will work on high-impact analytical and modelling projects that sit at the core of QS’s mission to improve higher education worldwide. You will develop models and pipelines that power university ranking simulations, track global skill movements, and predict student behaviour at scale.
You’ll collaborate closely with senior data scientists, engineers, and product teams, using QS’s rich global datasets to build robust, production-grade solutions. This role is ideal for someone who wants to deepen their technical expertise while contributing to work that influences institutions, learners, and policymakers around the world.
Role responsibilities
- Model Development
- Build and validate predictive, simulation and ranking-related models that inform global higher education and workforce insights.
- Develop models for student propensity, skills mobility, institutional performance and labour-market trends.
- Engineer and transform structured, semi-structured and longitudinal datasets into features suitable for production pipelines.
- Apply a range of statistical and machine-learning techniques (e.g., gradient-boosted models, graph methods, NLP, sequential simulation) to solve domain-specific problems.
- Design and run experiments to evaluate model performance and real-world impact.
- Develop metrics frameworks to benchmark ranking methodologies and predictive systems.
- Communicate analytical findings clearly to technical and non-technical stakeholders across the business.
- Work closely with Data Engineering to ensure modelling requirements are embedded into data pipelines and feature stores.
- Partner with Product and domain experts (rankings, labour-market intelligence, student mobility) to ensure models align with business and sector needs.
- Document workflows, modelling decisions, assumptions and evaluation results.
- Contribute to shared modelling components, best practices and reusable analytical assets.
Key skills and experience
- Proven experience in applied machine learning or data science.
- Proficiency in Python and SQL; experience with ML libraries such as scikit-learn, LightGBM, TensorFlow, PyTorch, MLflow.
- Strong grounding in statistics, feature engineering and data wrangling.
- Familiarity with cloud platforms (AWS preferred) and Git.
- Ability to tackle ambiguous analytical problems and work collaboratively in cross-functional teams.
- Bachelor's or Master's degree in a quantitative field (Computer Science, Statistics, Mathematics or related).
Please note, if you don’t meet all the criteria but believe you have the skills and passion to thrive in this role, we encourage you to apply.
QS is the world leader in higher education services, analytics, insights and intelligence. From consultancy to student mobility, academic partnerships to branding solutions, our services power both institutional and individual success.
We’re behind the world’s most widely read university rankings. Our QS World University Rankings reach hundreds of millions, shaping decisions and guiding futures. Since launching in 1990, our impact and influence have only grown. Today, we work with more than 2,000 of the world’s leading higher education institutions, over 12,000 employers, and governments seeking change and socioeconomic development through higher education.
Join QS and you’ll join an 800-strong community of problem-solvers, creators, collaborators and change-makers based in 40+ countries and 11 international offices, including Australia, Malaysia, India, Romania, Singapore, France, Germany, the USA and our headquarters in London.
With every talented new hire, business acquisition and bold initiative, we’re strengthening our reach and delivering even greater value to institutions and learners worldwide. Are you ready to shape the future of higher education?
We take investing in our people very seriously. As standard you will have:
- Competitive base salary
- Access to an annual bonus scheme (for qualifying roles only)
- 25 days annual leave, plus bank holidays – increasing to 27 days after 5 years
- Access to a Buy Holiday scheme allowing you to buy up to 5 additional holiday days per year
- Enhanced maternity and paternity leave
- Generous pension through Royal London
- Comprehensive private medical insurance and wellness scheme through Vitality
- Cycle to work scheme
- A vibrant social environment and multicultural and multinational culture
But that’s not all. Outside of these standard benefits we also offer resources to allow professional growth and wellness initiatives to nurture a healthy mindset:
- Free subscription to the Calm App – the #1 app for sleep, meditation, and relaxation
- A focus on welfare which is led by our global wellness team, with mental health first aiders globally
- Access to a variety of diversity and inclusion initiatives and groups
- Strong recognition and reward programs – including a peer-to-peer recognition platform, quarterly and annual QS Applaud Awards, Connect with your Career annual PD event
- Support for volunteering and study leave
- Free subscription to LinkedIn learning – with over 5000 courses and programmes at your fingertips
- Options to join our outstanding global Mentorship programme
Like what you’ve heard? Great, apply now.
As a candidate, we know the application and interview process can be daunting and so it’s important that you have a great experience with us. Our dedicated Talent Team will work hard to ensure you are fully informed at all stages and you are really excited by this opportunity to do meaningful work in the education space.
Equal opportunities
QS Quacquarelli Symonds is proud to be a fair and equal organisation where everyone has the same opportunity to achieve their full potential, irrespective of their background or personal attributes. We celebrate our diversity and believe through sharing our experiences we can learn from one another, be stronger together, and enable our business to thrive.
Data Scientist in London employer: QS Quacquarelli Symonds
Contact Detail:
QS Quacquarelli Symonds Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to current employees at QS or in the data science field on LinkedIn. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Prepare for your interview by brushing up on your technical skills. Practice explaining your past projects and how they relate to the role. We want to see your passion for data science shine through!
✨Tip Number 3
Showcase your problem-solving skills during interviews. Be ready to tackle hypothetical scenarios or case studies. Remember, it’s all about demonstrating how you think and approach challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team!
We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight relevant experience, especially in applied machine learning and data science. We want to see how your skills align with our mission at QS!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for higher education and how you can contribute to our goals. Be genuine and let us know why you’re excited about this opportunity.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python, SQL, and any ML libraries you’ve worked with. We love seeing candidates who can tackle complex problems and have hands-on experience with cloud platforms like AWS.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at QS!
How to prepare for a job interview at QS Quacquarelli Symonds
✨Know Your Data Science Stuff
Make sure you brush up on your machine learning techniques and statistical methods. Be ready to discuss your experience with Python, SQL, and any relevant libraries like scikit-learn or TensorFlow. Prepare to explain how you've applied these skills in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle some analytical problems during the interview. Think about how you would approach ambiguous situations and be ready to walk through your thought process. This will demonstrate your ability to think critically and work collaboratively.
✨Communicate Clearly
Practice explaining complex data concepts in simple terms. You’ll need to communicate findings to both technical and non-technical stakeholders, so being able to articulate your ideas clearly is key. Consider using examples from past experiences to illustrate your points.
✨Research QS and Its Values
Familiarise yourself with QS’s mission and values. Understand their focus on improving higher education and how your role as a Data Scientist fits into that vision. Showing genuine interest in the company will help you stand out and demonstrate that you’re a good cultural fit.