Data Engineer (Pharma) 1 YR Contract

Connecting Patients and Technology:

This Data Engineer role offers the unique opportunity to connect a passion for patients, clinical development and technology by contributing to the data engineering, data analysis, and AI/algorithm development activities in clinical studies to passively and objectively measure disease-relevant physiology in support of company's drug development and it's digital health products, as well as driving new software products and AI-powered features for Company's Sensor Cloud (i.e., Magnol. Ai platform). In this role, you will also be engaged with developing and validating digital biomarkers and ultimately digital measures, which can have significant regulatory, patient and clinical trial impact. The role is based in the Digital Health Innovation Hub being established in Singapore at the Company for Clinical Pharmacology.

Purpose of the Role:

The Data Engineer is responsible for the data infrastructure that carries digital health technology (DHT) and wearable sensor data from collection through to analysis-ready datasets, as well as trial-level digital device data strategy including acquisition, storage, retrieval, interchange, delivery and representation.

This requires an in-depth understanding of wearable sensor data collection, dataflow, data quality, data technology, dataset delivery, archiving and data standards.

This role partners with study teams from the earliest stages of study design, builds and operates the pipelines that move and process sensor data at scale, and maintains the automated quality checks that keep that data sound, traceable, and reproducible. It applies advanced data analysis tools and techniques to provide insights and actionable recommendations for the trial, utilizes algorithms to derive clinically meaningful digital measures, and explains findings to business audiences. The role also contributes to the team's ongoing research and automation efforts, applying new data, AI, and compute technologies to extend what company's digital health platform can do.

Primary Responsibilities:

• Support the early-stage design of digital health technology (DHT) studies, working with study teams to understand study objectives, the physiological signal the study is designed to detect, and the practice a realities of collecting data that will support that hypothesis.
• Design and Implementation: Lead the design, construction, and optimization of systems for data collection, data transformation, and quality verification for company's proprietary data sets and metadata.
• Use your architecture and programming skills to develop and implement data processing pipelines, data quality reporting, and pipeline monitoring resources for data ingestion and delivery of our proprietary data sets and machine learning outputs. The role encompasses end-to-end work such that the ideal candidate will have experience following raw data from ingestion all the way through to a completed feature data set prior to model scoring.
• Serve as a primary developer on the team's pipeline code, covering data movement, processing, aggregation, quality control, and the integration of AI capabilities.
• Algorithm development and testing: Apply heuristic algorithms, machine learning, and/or deep learning to develop new digital measures from sensor-based digital health technologies, e.g., daily activity recognition from continuous accelerometer / gyroscope data, sleep detection from Electroencephalography(EEG) and/or photoplethysmogram (PPG) data. Participate in model and algorithm development activities in a variety of areas including novel wearables, sleep detection, 3D environment reconstruction, and modern compute/GPU approaches for generating insights at scale.
• Innovation with AI and LLM: Design, develop, and test new software features for the next- generation sensor cloud (Company's Magnol. Ai platform). This includes new software products for the platform and the integration of LLMs to enhance Magnol. Ai data analysis capability for personalized medicine.
• Track the flow of DHT and Company's clinical trial data through the team's pipelines, monitoring study progress against expectations and flagging issues such as wear compliance, device malfunctions, or other emerging data problems.
• Manage data flow and storage at scale, maintaining an organized structure for raw and processed data and replicating it to wherever it needs to be accessed.
• Maintain and improve the team's automated data-quality-check systems, using them to catch issues early and drive root-cause analysis across studies.
• Ensure every pipeline run is tracked as an independent, fully-documente