Data Scientist

Data Scientist

Full-Time 36000 - 60000 ÂŁ / year (est.) No home office possible
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QS Quacquarelli Symonds

At a Glance

  • Tasks: Work on impactful data projects that shape global higher education.
  • Company: Join QS, a leader in higher education analytics and insights.
  • Benefits: Enjoy flexible hybrid work, competitive salary, and wellness initiatives.
  • Why this job: Make a real difference in education while growing your data science skills.
  • Qualifications: Experience in data science, proficiency in Python and SQL required.
  • Other info: Be part of a diverse team dedicated to empowering learners worldwide.

The predicted salary is between 36000 - 60000 ÂŁ per year.

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 your technical expertise while contributing to work that influences institutions, learners, and policymakers around the world.

Role responsibilities:

  • 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.

Documentation & Standards:

  • 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 (Meltwater 2023). 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.

We take investing in our people very seriously. As standard you will have:

  • 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

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 employer: QS Quacquarelli Symonds

At QS, we pride ourselves on being an exceptional employer that champions flexibility and personal growth. Our vibrant work culture fosters collaboration and respect, while our commitment to employee development is reflected in our gold accreditation from Investors in People. With a hybrid work model, generous benefits, and a focus on wellness and diversity, QS is the ideal place for Data Scientists looking to make a meaningful impact in the higher education sector.
QS Quacquarelli Symonds

Contact Detail:

QS Quacquarelli Symonds Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Data Scientist

✨Tip Number 1

Network like a pro! Reach out to current employees at QS on LinkedIn or through mutual connections. A friendly chat can give you insider info and might just get your foot in the door.

✨Tip Number 2

Prepare for the interview by brushing up on your technical skills. Be ready to discuss your experience with Python, SQL, and machine learning techniques. Show us how you can tackle real-world problems!

✨Tip Number 3

Don’t forget to showcase your soft skills! At QS, we value collaboration and communication. Be prepared to share examples of how you've worked in teams and communicated complex ideas to non-technical folks.

✨Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our awesome team at QS.

We think you need these skills to ace Data Scientist

Applied Machine Learning
Data Science
Python
SQL
Machine Learning Libraries (scikit-learn, LightGBM, TensorFlow, PyTorch, MLflow)
Statistics
Feature Engineering
Data Wrangling
Cloud Platforms (AWS preferred)
Git
Analytical Problem Solving
Collaboration in Cross-Functional Teams
Bachelor’s or Master’s Degree in a Quantitative Field

Some tips for your application 🫡

Show Your Passion: When writing your application, let your enthusiasm for data science and higher education shine through. We want to see how your skills can make a real impact at QS, so don’t hold back on sharing your motivation!

Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight relevant experience, especially in applied machine learning and data analysis. We love seeing how your background aligns with our mission to improve higher education.

Be Clear and Concise: Keep your application clear and to the point. Use straightforward language to explain your skills and experiences. We appreciate clarity, especially when it comes to complex topics like data modelling and analytics.

Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to see what you bring to the table!

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. They’ll likely ask you to explain how you've applied these skills in real-world scenarios.

✨Understand QS's Mission

Familiarise yourself with QS’s goals and values. They’re all about improving higher education worldwide, so think about how your work as a Data Scientist can contribute to that mission. Be prepared to share your thoughts on how data can drive positive change in the education sector.

✨Prepare for Technical Questions

Expect some technical questions or even a practical test during the interview. Practice explaining your thought process when solving analytical problems and be ready to showcase your problem-solving skills. It’s all about demonstrating your ability to tackle complex data challenges.

✨Show Your Collaborative Spirit

QS values collaboration, so highlight your experience working in cross-functional teams. Share examples of how you’ve partnered with others to achieve common goals, especially in projects involving data engineering or product development. They want to see that you can communicate effectively with both technical and non-technical stakeholders.

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