Senior Data Science Manager

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 demonstratedtrack recordof translating complex, multi-modal data into testable hypotheses and actionable insights in support of clinical development activities and decisions.

You will driveexploratoryand 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) and1+ yearsof academic/industry experience;orMaster's Degreein a relevant quantitative field and3+ yearsof 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 andvalidatingstatistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes

• Experience in the application of AI/ML andproficiencyin 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 inleveraginginnovative approaches toexpeditedrug 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 ofestablishingstrong working relationships across the organization

Preferred Qualifications

• Experience withgenomics, proteomics, imaging, flow cytometry, or immunobiology datasetsfrom clinical trials is highly preferred

• Experience withNLPis highly preferred

• Experience withSurvival Analysisand time-to-event modeling is highly preferred

• Experience withcausal ML and explainable AIis 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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Originally posted on Himalayas