Principal Data Scientist

This position will provide technical expertise in integrating biomedical signal analysis, probabilistic modelling, and scalable data pipelines, ensuring high-quality, reproducible insights from complex biomedical high frequency data, including electrophysiological, AI-based 24/7 video recordings and wearable-derived signals, tabular data, e.g. demographic and genetic, and EHR. Applies cutting-edge data science approaches while adhering to project timelines. As a Principal Data Scientist, your responsibilities will include:

• Identify, access, and integrate diverse data sources, including clinical study data, observational data, and real-world healthcare datasets, ensuring high-quality data extraction and pre-processing.
• Develop and implement signal processing methods for data curation and feature/pattern extraction from longitudinal data, with particular focus on high frequency data (e.g., electrophysiological and wearable signals), for clinical and research applications.
• Develop and implement machine learning model for clinical applications, including disease phenotyping, and predictive modelling.
• Apply advanced computational techniques such as time-series analysis, feature engineering, and probabilistic modeling to enhance biomedical data interpretation.
• Develop and maintain scalable, high-performance data pipelines systems that align with business needs and industry best practices.
• Validate and benchmark machine learning models against established state-of-the-art methods, ensuring clinical relevance and interpretability.
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/Masters or Bachelor's degree in computer science, biomedical engineering, mathematics, data science or a related discipline.
• Experience within the pharmaceutical or healthcare industry
• Good written and verbal communication skills, including the ability to write technical reports, and concise summaries of complex findings.
• Proven expertise in advanced analytical research techniques, including machine learning and signal processing, particularly in biomedical/clinical data applications.
• Proficient in R or Python, with hands-on experience in machine learning and data analysis.
• Proficiency with data extraction and integration techniques, including APIs, relational databases, cloud-based solutions, ensuring seamless access and transformation of clinical, observational, and healthcare data.
• Proficiency with software engineering best practices for version control, reviewing, and testing.
• Competent in the use of cloud based technologies (for example AWS).
• Expertise in handling diverse healthcare data formats, including clinical data standards, electronic health records (EHR), electrophysiological signals, and wearable device data, ensuring compliance with industry best practices.
• Moderate experience in pharmaceutical and biotech consulting, ensuring alignment with GxP compliance, regulatory guidelines.
• Strong proficiency in data visualization, including R Shiny, Matplotlib, and interactive dashboard tools, to support clear, data-driven decision-making.
• Maintaining effective client communication and stakeholder engagement, ensuring timely updates, well-documented requirements, and alignment with business objectives.
• Ability to make timely decisions on technical project issues.