Data Scientist – Digital Health & Sensor Data Analytics

InnoCellence; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

InnoCellence is seeking a Data Scientist to derive insights from digital device sensor data in clinical studies and to develop algorithms for real-time processing and decision support.

The role emphasizes collaboration with customers, building models, and delivering visualizations and reports that translate data into business outcomes. A PhD and 5+ years in data science are preferred.

Qualifications

• PhD in CS/Statistics/Math or related field; 5+ years in data science focusing on sensor data or digital health.
• Strong background in time-series, signal processing, and feature extraction.
• Proficient in Python or R for data analysis and ML; experience with TensorFlow/PyTorch.

Responsibilities

• Collaborate with customers to frame questions, test hypotheses with clinical study data.
• Develop and implement ML algorithms for processing sensor data and derive novel digital measures.
• Design and implement LLM/RAG pipelines and related AI tooling.
• Preprocess large sensor datasets, address noise, missing data, and time-series alignment.
• Validate results for accuracy, performance, and real-time scalability; report findings clearly.

Skills

Time-series analysis

Sensor data analysis

Python/R programming

TensorFlow/PyTorch

Communication skills

Education

Ph.D. in Computer Science or Statistics or related field

5+ years of data science experience

Tools

Hadoop

Spark

Cloud platforms (AWS/Azure)

LLMs / RAG pipelines

Job description

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.
• Design, develop, and implement solutions leveraging Large Language Models (LLMs) and Generative AI technologies, including prompt engineering, model fine-tuning, and Retrieval-Augmented Generation (RAG) pipelines.
• 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.
• 5+ 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

• 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.