QS Quacquarelli Symonds

Data Scientist

QS Quacquarelli Symonds

full-time

Posted on:

Location Type: Hybrid

Location: London • United Kingdom

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Tech Stack

AWSCloudPythonPyTorchSQLTensorflow

About the role

  • 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

Requirements

  • 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)
Benefits
  • 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
  • Free subscription to the Calm App – the #1 app for sleep, meditation, and relaxation
  • 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
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
machine learningdata sciencePythonSQLscikit-learnLightGBMTensorFlowPyTorchMLflowstatistics
Soft Skills
analytical problem solvingcollaborationcommunication
Certifications
Bachelor's degreeMaster's degree