Data Scientist

🔒 Confidential Employer
Posted 24 March 2026
LOCATION
London
TYPE
Full-time
LEVEL
Mid-Senior level
CATEGORY
Technology
This role is not offered with visa sponsorship, though the employer is a licensed UK sponsor

SKILLS

Python SQL Machine Learning Statistics Feature Engineering Data Wrangling AWS Git

FULL 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.

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.

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.

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).
Sign up free — access 45,000+ UK sponsor-licensed jobs