Data Scientist

Applicants must have the existing right to work in the UK. This role is not eligible for visa sponsorship.

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.

At QS, you’ll be responsible for implementing real change in the international higher education landscape. You’ll take on meaningful challenges that see a positive impact across the business and the wider sector.

We’re confident you’ll feel right at home here. QS was named as one of Newsweek’s Top 100 Most Loved Workplaces® in the UK (October 2023), recognising the respect, trust and appreciation that drive our culture every day. And as a gold-accredited Investors in People organisation – putting us among the top 28% of workplaces globally – it’s official: QS is a place where everyone can thrive.

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.

• 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.

• Proven experience in applied machine learning or data science.
• Proficiency in Python and SQL; experience with ML libraries such as scikit‑learn, Light
  
  GBM, Tensor Flow, 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…