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The Principal Data Scientist will integrate diverse clinical and healthcare datasets to develop advanced machine learning models and signal processing methods. They are responsible for building scalable data pipelines and ensuring high-quality, reproducible insights for clinical research applications.
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, longitudinal analysis, feature engineering, and probabilistic modelling 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 interoperability.
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:
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 physiological data, imaging, and high frequency sensor data).
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.
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.
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