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About the Role
This role involves analyzing and interpreting complex data for research projects, clinical trials, and customer studies, and shaping lung disease research through the development of impactful statistical models. You will collaborate with cross-functional teams and work with major pharmaceutical companies and leading research institutions.
Responsibilities
- Provide statistical expertise across all initiatives, including clinical study design, clinical trial analysis and business development activities.
- Design, expand, and maintain robust data processing pipelines, statistical analysis and machine learning workflows, and data visualisation tools for experimental and clinical research.
- Develop and apply supervised and unsupervised machine learning approaches, including predictive modelling and clustering, to extract insights from increasingly large and complex clinical and real-world datasets.
- 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 directly to clients and other stakeholders.
- Contribute to the development of scientific materials, including abstracts, posters, conference presentations and manuscripts for publication, as required.
- Collaborate as an innovative and creative member of a multidisciplinary team, driving novel approaches to advance the company’s mission.
Requirements
- 2+ years of industry experience in applied data science
- 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.
- Experience applying supervised and unsupervised machine learning techniques to real-world datasets, including predictive modelling, classification and clustering, with an understanding of appropriate model selection, validation and evaluation.
- Strong communication and presentation skills with the ability to communicate complex analytical findings clearly to clients, clinicians and other technical and non-technical stakeholders.
- Ability to contribute to scientific communications, including abstracts, posters, presentations and manuscripts.
- 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. vectorization, memory management)
- Expert skills with data wrangling and cleaning large datasets
Skills
- Python
- Pandas
- Jupyter
- Scikit-learn
- NumPy
- SciPy
- Matplotlib
- Seaborn
- Plotly
- Git
- Data wrangling
- Data cleaning
- Statistical modeling
- Machine learning
- Predictive modeling
- Clustering
- Regression models
- Supervised learning
- Unsupervised learning
- Probability
- Statistics
- Survival analysis
- Cox regression
- Kaplan-Meier estimation
- Data preprocessing
- Feature engineering
- Cross-validation
- Regularization (L1/L2)
- Feature selection
- Model evaluation
- AUC
- Precision
- Recall
- Calibration
- Communication
- Presentation
Location
- Cambridge
- London
Work Type
- Full-time
Experience Level
- 2+ years of industry experience in applied data science
Education Level
- Degree in statistics, mathematics or a related quantitative or scientific subject
Benefits
- Annual bonus plan
- Private medical insurance
- Life insurance
- Contributory pension scheme
- 25 days annual leave
- Bank holidays
- Enhanced maternity leave
About the Company
- Qureight’s mission is to accelerate clinical trials and ensure breakthroughs in lung and heart disease reach patients without delay.
- Our AI-powered data and imaging curation platform enables the analysis of clinical imaging and other healthcare data, helping our customers bring treatments to market, faster.
- We’re looking for talented people who want their work to matter.
- With offices in Cambridge and London, you’ll join our multidisciplinary team of clinicians, scientists, and engineers.
- What unites us is our open culture, continuous learning mindset, and a shared mission to help biopharma run faster, smarter trials.
Equal Opportunity
- Everyone is welcome at Qureight. We are an equal opportunities employer and encourage applications from all suitably qualified candidates regardless of age, disability, ethnicity, sex, gender reassignment, religion or belief, sexual orientation, marriage and civil partnership, or pregnancy and maternity.
- Women and other underrepresented groups may be less likely to apply for a role unless they meet all or nearly all of the requirements. If this applies to you, we still encourage you to apply – you may be a great fit, even if you don’t meet every qualification. We’d love to hear from you.
- If you require any adjustments to the application or selection process, please let us know. We will be happy to support you.