We are seeking a talented and experienced Data Scientist to join our team. The ideal candidate will be passionate about utilizing data science methodologies to derive insights from digital device sensor data coming from clinical studies and contribute to the development and implementation of algorithms in this area. As a Data Scientist, you will work closely with customers to analyze data and build models and tools that enhance understanding of clinical outcomes and solve their business problems.

Responsibilities:

Collaborate with customers to identify key questions, develop hypotheses, and test those hypotheses with clinical study data.
Develop and implement machine learning algorithms for processing and interpreting sensor data (e.g., accelerometers, heart rate monitors, etc.). Research and develop novel digital measures with clinical significance.
Apply data preprocessing techniques to optimize large sets of sensor data processing and analysis, handling issues like noise, missing data, and time-series synchronization.
Validate and interpret results to ensure accuracy, performance and scalability in real-time processing of sensor streams.
Communicate findings and insights to stakeholders through clear and compelling visualizations, reports, and presentations.
Work closely with cross-functional teams to develop data-driven solutions to address business needs.
Stay up-to-date with the latest advancements in data science and clinical research to continuously improve methodologies and approaches.

Requirements:

Ph.D. in Computer Science, Statistics, Mathematics, or related field.
3+ years of experience in data science, with a focus on sensor data analysis or digital health.
Strong understanding of time-series analysis, signal processing, and feature extraction techniques.
Proficiency in programming languages and libraries (e.g, Python/R, TensorFlow/Pytorch) for data analysis and machine learning.
Hands-on experience with big data platforms and tools (e.g., Hadoop, Spark, cloud environments).
Strong problem-solving and analytical skills.
Excellent communication and collaboration skills with the ability to work effectively in a team environment.

Preferred Qualifications:

Hands-on experience with Large Language Models (LLMs) or Generative AI projects, such as prompt engineering, fine-tuning, or implementing Retrieval-Augmented Generation (RAG) pipelines
Familiarity with cloud-based deployment platforms such as AWS, Azure or related and their data pipeline development.
Experience working in the healthcare or pharmaceutical industry, particularly in clinical trials or digital health devices.
Proven experience in taking algorithms from research to production, including considerations for scalability, performance, and ease of integration.
Understanding of clinical trial workflows and regulatory requirements related to digital health.

Data Scientist - Digital Health & Sensor Data Analytics