Description review
PhysioNet Competitor (Biomedical Signal Processing & ML)
micro1 · United States · back to the listing
HR standards
78/100
solid
Title ↔ description
62/100
needs work
Reads as
Data Scientist
86% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
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Role Title: PhysioNet Competitor (Biomedical Signal Processing & ML)
Pay: $60–$120/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
Schedule: Flexible-you choose the hours and days you work, including weekends if desired
micro1 is engaging specialists with experience in the PhysioNet Challenge or comparable biomedical signal processing and machine learning competitions. In this role, you will apply your scientific programming, signal processing, and modeling expertise to create, solve, and evaluate advanced technical problems that help improve next-generation AI systems.
We are looking for people who have worked directly with physiological signals and can turn noisy, real-world data into a rigorous and reproducible analysis. Strong candidates will be able to explain the methods they developed, the experiments they ran, the technical decisions they personally owned, and how they validated their results. No prior experience working on AI training projects is required.
Scope of Work
• Create technically rigorous, self-contained problems involving biomedical signals, time-series data, signal processing, and machine learning.
• Develop clear reference solutions using Python, MATLAB, or another appropriate scientific computing environment.
• Work with tasks involving signal preprocessing, filtering, segmentation, feature extraction, representation learning, classification, or forecasting.
Preferred Qualifications
• Participation in a PhysioNet/Computing in Cardiology Challenge or a comparable biomedical signal processing or scientific machine learning competition.
• Strong competition results and evidence of a substantive personal technical contribution.
• Public evidence of your work through an official team result, conference paper, technical report, open-source repository, or meaningful code contribution.
• Hands-on experience with physiological signals such as ECG, EEG, PPG, EMG, respiratory signals, polysomnography, or other clinical time-series data.
Process
• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete the hiring manager review.
Compensation Structure
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
Minimum submission requirements apply.
Start Timeline & Availability
• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
Originally posted on Himalayas
Pay: $60–$120/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
Schedule: Flexible-you choose the hours and days you work, including weekends if desired
micro1 is engaging specialists with experience in the PhysioNet Challenge or comparable biomedical signal processing and machine learning competitions. In this role, you will apply your scientific programming, signal processing, and modeling expertise to create, solve, and evaluate advanced technical problems that help improve next-generation AI systems.
We are looking for people who have worked directly with physiological signals and can turn noisy, real-world data into a rigorous and reproducible analysis. Strong candidates will be able to explain the methods they developed, the experiments they ran, the technical decisions they personally owned, and how they validated their results. No prior experience working on AI training projects is required.
Scope of Work
• Create technically rigorous, self-contained problems involving biomedical signals, time-series data, signal processing, and machine learning.
• Develop clear reference solutions using Python, MATLAB, or another appropriate scientific computing environment.
• Work with tasks involving signal preprocessing, filtering, segmentation, feature extraction, representation learning, classification, or forecasting.
Preferred Qualifications
• Participation in a PhysioNet/Computing in Cardiology Challenge or a comparable biomedical signal processing or scientific machine learning competition.
• Strong competition results and evidence of a substantive personal technical contribution.
• Public evidence of your work through an official team result, conference paper, technical report, open-source repository, or meaningful code contribution.
• Hands-on experience with physiological signals such as ECG, EEG, PPG, EMG, respiratory signals, polysomnography, or other clinical time-series data.
Process
• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete the hiring manager review.
Compensation Structure
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
Minimum submission requirements apply.
Start Timeline & Availability
• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
Originally posted on Himalayas