Lead clinical studies by providing statistical support, employing advanced analytical methods, and collaborating with cross-functional teams to optimize clinical development processes.
Who Are You?
An experienced Principal Biostatistician with a passion for clinical development and analysis, adept at utilizing advanced statistical methods, you will lead studies across your region. You are excited and enthusiastic, motivate your teams to do great work and collaborate easily with your clients. You never settle for what is, but always push clinical development forward to what it could be. You motivate others to do the same.
Sponsor-dedicated:
Working fully embedded within one of our pharmaceutical clients, with the support of Cytel right behind you, you'll be at the heart of our client's innovation. You will be dedicated to one of our global pharmaceutical clients; a company that is driving the next generation of patient treatment, where individuals are empowered to work with autonomy and ownership. This is an exciting time to be a part of this new program.
As a Principal Biostatistician, your responsibilities will include:
- Lead and contribute to advanced real-world evidence (RWE) analyses using large-scale real-world data (RWD) to support clinical development, regulatory strategy, HEOR, and payer decision-making.
- Design observational studies and target trial emulations leveraging diverse data sources including EHR (Electronic Health Record), claims, registries, genomics, and digital health data.
- Apply modern causal inference and statistical learning methods (e.g., propensity score techniques, inverse probability weighting, survival and longitudinal models, representation learning) to address confounding, bias, and missing data.
- Develop and deploy machine learning pipelines for prediction, patient stratification, phenotyping, and NLP (Natural Language Processing) on structured and unstructured healthcare data.
- Integrate AI/ML outputs with traditional statistical frameworks to ensure interpretability, scientific rigor, and regulatory readiness.
- Build scalable, reproducible analytical workflows in Python and/or R, following best practices in software engineering, documentation, and quality control.
- Collaborate closely with clinical scientists, epidemiologists, data engineers, and HEOR partners to define fit-for-purpose analytical strategies.
- Translate complex quantitative results into actionable insights for technical and non-technical stakeholders.
- Contribute to governance readiness, regulatory submissions, audits, and scientific dissemination.
- Deliver high-quality analytical outputs within agreed timelines in a matrixed, global FSP environment.
Here at Cytel we want our employees to succeed and we enable this success through consistent training, development and support. To be successful in this position you will have:
- PhD in Statistics, Applied Mathematics, Data Science, or related quantitative field;
or MS with 3+ years of relevant industry experience. - Strong grounding in mathematical statistics, probability, and statistical modeling.
- Demonstrated experience applying machine learning or AI methods to large or high-dimensional datasets.
- Proficiency in Python and/or R; familiarity with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow) preferred.
- Solid understanding of causal inference and observational study design.
- Experience working with large-scale real-world data.
- Strong software engineering mindset, including reproducibility, version control, and documentation.
- Excellent communication skills and ability to collaborate effectively in cross-functional, global teams
- Experience with healthcare RWD (claims, EHR, OMOP/CDM, registries).
- Familiarity with cloud or big-data environments (SQL, Spark, Databricks, AWS, Azure, GCP).
- Experience with Bayesian modeling or probabilistic machine learning.
- Publications or applied research in ML + healthcare or RWE.
- Interest in translating academic ML into real-world, regulatory-grade analytics.
Top Skills
Deep Learning
Machine Learning
Python
R
SAS
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