Data Scientist
Role details
Job location
Tech stack
Job description
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.
Experimentation & Analysis
- 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.
Collaboration
- 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.
Requirements
Do you have a Master's degree?, Applicants must have the existing right to work in the UK. This role is not eligible for visa sponsorship., * 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.
Benefits & conditions
- 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 schemeA 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 fingertipsOptions to join our outstanding global Mentorship programme