[Hiring] Principal Data Scientist @Cytel

Role Description

An experienced Data scientist, ML engineer or computational scientist specializing in signal processing, ML, and clinical data science to support clinical studies and health research. 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 and wearable-derived signals
• Tabular data (e.g., demographic and genetic)
• EHR

Applies cutting-edge data science approaches while adhering to project timelines.

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.

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 signals
• AI-based 24/7 video recordings
• Wearable-derived signals
• Tabular data (e.g., demographic and genetic)
• EHR

Applies cutting-edge data science approaches while adhering to project timelines.

Responsibilities

• 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 models 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 pipeline 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.

Qualifications

• 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 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.
• 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.