Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.
Who Are You?
An experienced RWE 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 an RWE Biostatistician, your responsibilities will include:
- Lead and support advanced RWE analyses using large-scale RWD to inform clinical development, regulatory strategy, HEOR, and payer decisions.
- Review statistical analysis plans and provide input for observational studies and target trial emulations using diverse data sources, including EHRs, claims, registries, genomics, and digital health data.
- Apply causal inference and statistical learning methods, such as propensity scores, inverse probability weighting, survival and longitudinal models, and representation learning, to address confounding, bias, and missing data.
- Develop and implement machine learning pipelines for prediction, patient stratification, phenotyping, and NLP using structured and unstructured healthcare data.
- Combine AI/ML outputs with traditional statistical approaches to support interpretability, scientific rigor, and regulatory readiness.
- Build scalable, reproducible analytical workflows in Python and/or R, applying best practices in software engineering, documentation, and quality control.
- Collaborate with clinical scientists, epidemiologists, data engineers, and HEOR partners to define fit-for-purpose analytical strategies.
- Translate complex quantitative findings into actionable insights for technical and non-technical stakeholders.
- Support governance readiness, regulatory submissions, audits, and scientific dissemination.
- Deliver high-quality analytical outputs on agreed timelines within a global, matrixed 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 3-5+ 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 Statistical programming languages including SAS, R and Python: familiarity with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow) preferred.
- Solid understanding of causal inference and observational study design.
- Excellent communication skills and ability to collaborate effectively in cross functional, global teams.
- Experience with large-scale real-world data (administrative 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.
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