When our values align, there's no limit to what we can achieve. At Parexel, we all share the same goal - to improve the world's health. From clinical trials to regulatory, consulting, and market access, every clinical development solution we provide is underpinned by something special - a deep conviction in what we do. Each of us, no matter what we do at Parexel, contributes to the development of a therapy that ultimately will benefit a patient. We take our work personally, we do it with empathy and we're committed to making a difference. We are looking for a candidate with strong computational, statistical, and biological capabilities and a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights in support of clinical development activities and decisions. You will drive exploratory and confirmatory analyses (both hypothesis-generating and hypothesis-driven), across diverse data types generated in drug development, including clinical trial data, genomics, proteomics, imaging, flow cytometry, and other biomarker modalities. You will define and implement approaches, processes, algorithms, and pipelines that support the analytics, visualization, and decision support needs of drug development scientists and project teams, while collaborating closely with Biostatistics leads, Translational and Clinical Scientists, and cross-functional partners across the organization. Key Qualification, Experience and Skills Requirements; Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) and 1+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 3+ years of industry experience Strong experience in data science and statistical analysis with data generated from clinical trials or electronic health records, particularly in application to pharma R&D Experience in developing and validating statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes Experience in the application of AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks) Familiarity with clinical trial design, drug development processes, and the role of biomarkers in regulatory and clinical decision-making Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects Strong problem-solving and collaboration skills, and rigorous and creative thinking Excellent communication, data presentation, and visualization skills Capable of establishing strong working relationships across the organization

Preferred Qualifications

Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials is highly preferred Experience with NLP is highly preferred Experience with Survival Analysis and time-to-event modeling is highly preferred Experience with causal ML and explainable AI is highly preferred Knowledge of molecular biology and understanding of disease pathways is preferred Experience with real-world data (RWD/RWE) sources and associated analytical methods is preferred Familiarity with digital health data and wearable/sensor-derived data types is a plus Experience with scalable compute and deployment patterns, including cloud-based platforms and parallelization for large-scale data processing and model training is a plus Outline of Daily Key Responsibilities;

• Data Science & Analytics
• Data Engineering & Reproducibility
• Collaboration & Technical Contribution

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