Data Science and Signal Processing Intern

About the Role

CranioSense is seeking a highly motivated Bachelor’s or Master’s-level Data Science & Signal Processing Intern to support preparation and transformation of complex physiological waveform and sensor datasets. The ideal candidate will be hands-on with raw data preparation and quality inspection, writing basic data conditioning and analysis code, investigating signal quality, and converting raw photoplethysmography (PPG) waveforms into candidate features for use by CranioSense's senior data science and algorithm team. The intern will work closely with CranioSense's VP of Data Science and other technical leads, providing the data preparation, signal-processing, coding, and feature-engineering foundation needed for downstream model development and validation. Support light literature review alongside data preparation work, surfacing relevant papers on optical-based signal processing, biomedical feature engineering, and physiological modeling to build familiarity with the research context informing the senior team’s development. This is a hands-on, collaborative role with meaningful ownership of analytical workflows and outputs. This internship offers strong potential for conversion into a full-time role following successful completion.

Key Responsibilities

• Prepare, organize, assess and quality-check physiological waveform (and other relevant bio-signals), sensor, clinical, and metadata datasets
• Develop preprocessing and signal-conditioning code to filter, normalize, segment, synchronize, and prepare raw physiological data streams
• Explore and mine multi-site PPG and related physiological signals to identify patterns, artifacts, data-quality issues, and candidate features
• Transform raw PPG waveforms into reproducible features and structured datasets for use by senior data scientists in model development and validation
• Write reusable Python/Matlab code and scripts to automate data preparation, feature extraction, visualization, and exploratory analysis
• Support dataset assembly, labeling, documentation, versioning, and traceability across clinical and development activities
• Perform exploratory analyses and visualizations to characterize feature behavior, cohort differences, and signal quality
• Collaborate with the VP of Data Science, product, engineering, and clinical teams to troubleshoot data and signal-processing issues
• Document methods and findings clearly for the broader data science team

Requirements & Tech Stack

• Pursuing or recently completed a Bachelor’s or Master’s degree in Biomedical Engineering, Electrical Engineering, Computer Science, Data Science, Applied Mathematics, Physics, Computational Biology, or a related quantitative discipline
• Strong Python/Matlab skills and experience working with scientific, engineering, biomedical, or time-series datasets
• Experience with basic signal processing, feature extraction, data quality assessment, preprocessing, and exploratory analysis of noisy real-world data
• Experience with physiological waveforms, PPG, biophotonics, optical sensing, or other biomedical sensor data preferred (ECG, ABP etc.)
• Comfort with NumPy, pandas, SciPy, scikit-learn, visualization tools, and related scientific-computing libraries
• Experience with Git, reproducible analysis workflows, and cloud-based computing environments preferred
• Experience with structured datasets, SQL, or modern data tooling preferred
• Strong attention to detail and ability to investigate complex datasets, debug analytical pipelines, and follow problems through to resolution
• Strong analytical, critical-thinking, and problem-solving skills with a willingness to dig into data and iterate
• Excellent written and verbal communication skills

Qualifications

• Resume and a brief statement of interest outlining relevant experience in Python, signal processing, physiological data, feature engineering, or biomedical data science.

Preference may be given to candidates registered through the Massachusetts Life Sciences Center (MLSC) Data Science Internship Program.