Analyze and preprocess noisy, real-world health and sensor data using statistical and signal processing techniques
Design and implement signal cleaning, filtering, denoising, and feature extraction pipelines
Apply time-series analysis, spectral analysis, and statistical modeling to interpret complex data patterns
Develop and train machine learning models on preprocessed signals for prediction, classification, or anomaly detection
Evaluate data quality and identify artifacts, outliers, and inconsistencies in raw datasets
Collaborate with cross-functional teams to understand data sources, sensor behavior, and real-world constraints
Validate models using appropriate statistical metrics and experimental design principles
Optimize algorithms for performance, robustness, and scalability
Document assumptions, methodologies, and findings clearly for technical and non-technical stakeholders
Iterate rapidly on experiments, incorporating feedback from real-world data observations
Stay updated with advances in signal processing, applied statistics, and ML techniques relevant to healthcare data

About Company: We've worked at the intersection of healthcare and information technology to connect people and digitize healthcare systems around the world. We support our clients by surfacing data that enables them to make informed decisions for better management of operations, empowering providers with the information they need to provide smarter care and transform the way health care is delivered.

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