Data Scientist

VivoSense, Inc.

4d•$100,000 - $150,000•Remote

About The Position

VivoSense is a science-first digital health company building the future of patient-centered evidence through advanced biosensor analytics. We partner with pharmaceutical companies, biotechnology organizations, and academic researchers to transform high-resolution sensor signals into validated digital measures that improve clinical trials and accelerate the development of new therapies. Our work spans the full scientific lifecycle – from signal acquisition and algorithm development to clinical validation, regulatory evidence generation, and delivery of analysis-ready datasets for global clinical trials. We're looking for curious, versatile data scientists who enjoy solving difficult scientific problems. This is intentionally an open-level position. Whether you're an early-career scientist eager to learn or an experienced technical leader, we're interested in people who enjoy wearing multiple hats and working across disciplines. One day you may be validating a novel digital endpoint using clinical trial data. The next you might be developing a machine learning algorithm for ECG signals, developing production-quality Python code for a wearable sensor pipeline or troubleshooting unexpected edge cases in real-world sensor data. Success in this role requires someone who enjoys ambiguity, continuously learning new technologies, and moving comfortably between science, statistics, software, and clinical research.

Requirements

MSc or PhD in Biomedical Engineering, Data Science, Biostatistics, Computer Science, Applied Mathematics, Physics, or related quantitative field.
Strong programming
Coursework or research involving signal processing, machine learning, statistics, or physiological data.
Curiosity and desire to
Several years of experience developing algorithms or digital measures for healthcare or clinical research.
Experience leading technical projects from concept through
Experience mentoring junior
Ability to independently design analytical approaches for novel scientific
Experience interacting with external collaborators or

Nice To Haves

Python
Time-series analysis
Signal processing
Statistical modeling
Clinical trials
Wearable sensor data
Digital biomarkers
Machine learning
Physiological and kinematic signal analysis
Git/version control
Data visualization

Responsibilities

Develop novel digital measures from high-resolution sensor
Design and evaluate algorithms that transform raw sensor signals into clinically meaningful outcomes through signal processing, feature engineering, and statistical
Design and execute analytical validation and clinical validation
Evaluate reliability, validity, responsiveness, and clinical meaningfulness of digital
Work with multimodal physiological signals including accelerometry, PPG, respiratory bands, ECG, gyroscope, temperature, and other wearable sensors.
Develop filtering, segmentation, feature extraction, and quality assessment
Handle challenging real-world data including missing data, motion artifact, signal quality issues, and edge cases.
Develop robust Python code supporting analytical
Build reusable analysis
Translate raw sensor streams into structured, SDTM-like datasets suitable for downstream statistical analysis.
Write maintainable, well-tested code supporting clinical trial
Collaborate with biostatisticians, software engineers, clinical scientists, and product
Support scientific publications, conference presentations, regulatory interactions, and client deliverables.