Data Scientist

Qureight Ltd
Cambridge
9 months ago
Applications closed

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** Please no recruitment agents**

We are looking for a talented and driven individual to join our forward-thinking data science team. In this role, you will play a vital part in analysing and interpreting complex data across research projects, clinical trials, and customer studies. You will also contribute to shaping the future of our lung disease research by developing impactful statistical models. Collaborating closely with cross-functional teams, you will have the opportunity to work alongside major pharmaceutical companies and leading research institutions worldwide.

Requirements

Key Responsibilities:

  • Provide statistical expertise across all initiatives, including clinical study design and business development activities. 
  • Design, expand, and maintain robust data processing pipelines, statistical analysis workflows, and data visualisation tools for experimental and clinical research. 
  •  Develop new and refine existing statistical analysis processes in collaboration with stakeholders and clinicians. 
  • Prepare and deliver study design protocols, analysis reports, presentations, and other materials to effectively communicate insights and findings to stakeholders and customers. 
  • Collaborate as an innovative and creative member of a multidisciplinary team, driving novel approaches to advance the company’s mission


Experience, Skills and Knowledge:

  • 2+ years of industry experience in applied data science 
  • Full right to work in the UK without restriction, time limit, or sponsorship 
  • Deep understanding of probability and statistics, including power and sample size calculations, hypothesis testing, parametric and non-parametric methods, and survival analysis techniques such as Cox regression and Kaplan-Meier estimation. 
  • Proven experience applying statistical methodologies and best practices—such as data preprocessing, feature engineering, and method selection—to real-world datasets, including clinical trial data, particularly within the pharmaceutical sector or in collaboration with contract research organizations. 
  • Skilled in leveraging regression models—particularly linear and logistic regression—for both inference and prediction, applying techniques such as cross-validation, regularization (L1/L2), feature selection, and model evaluation using metrics including AUC, precision, recall, and calibration. 
  • Strong communication and presentation skills and a collaborative mindset. 
  • High level of competence with Python (Pandas, Jupyter, Scikit-learn, NumPy, SciPy, Matplotlib, Seaborn, Plotly) 
  • Ability to write clean, efficient and maintainable Python code following best practices, including modular design, version control (Git), clear documentation, error handling, testing, and performance-conscious data processing (e.g. vectorisation, memory management) 
  • Expert skills with data wrangling and cleaning large datasets


Optional Experience, Skills & Knowledge:

  • Experience of more advanced statistical models such as mixed-effects models is a plus
  • Ability to use matching techniques to create synthetic arms in clinical trial cohorts
  • Proficiency with command line
  • Experience of cloud providers (AWS preferred)

Qualifications & Education:

  • Degree in statistics, mathematics or a related quantitative or scientific subject

Benefits

The chance to join a friendly, motivated group of people on a mission for universal good in healthcare.

  • Flexible working hours.
  • Hybrid working policy.
  • Competitive salary.
  • 25 days annual leave, plus bank holidays.
  • Annual enrichment programme.
  • Salary exchange pension scheme.
  • Private medical insurance (including pre-existing).
  • Medical Cash Plan benefit.
  • Death In Service benefit.
  • Discretionary employee share options scheme.
  • Opportunities for professional development and academic collaborations in a vibrant and fast-acting company.
  • Co-working passes.

Qureight reserve the right to amend or remove any of these at any given time.

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