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Eeg Data Science Jobs (NOW HIRING)

The successful candidate will join the data science team and will be responsible for design and development of new state-of-the-art classification algorithms that will take the field of EEG ...

Data Scientist

Sunnyvale, CA · On-site

$184K - $210K/yr

The successful candidate will join the data science team and will be responsible for design and development of new state-of-the-art classification algorithms that will take the field of EEG ...

The successful candidate will join the data science team and will be responsible for design and development of new state-of-the-art classification algorithms that will take the field of EEG ...

EEG Technician

New York, NY · On-site

$31 - $38/hr

Data Quality Control: Monitor patient EEG data, recognize artifacts, and take steps to eliminate ... healthcare, scientific, technology, and government industries. Through our core purpose of ...

Develop performant, distributed pipelines and algorithms for EEG data processing and analysis ... Computer Science Fundamentals: Solid grasp of CS principles, algorithmic thinking, and problem ...

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Eeg Data Science information

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How much do eeg data science jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for eeg data science in the United States is $34.48, according to ZipRecruiter salary data. Most workers in this role earn between $26.44 and $40.38 per hour, depending on experience, location, and employer.

What is EEG data science?

EEG data science is the application of computational, statistical, and machine learning techniques to analyze and interpret data collected from electroencephalography (EEG). EEG measures electrical activity in the brain, and data scientists in this field work to extract meaningful patterns, features, or biomarkers from the complex signals. Their work supports research and clinical applications, such as diagnosing neurological disorders, studying brain function, and developing brain-computer interfaces.

What are the key skills and qualifications needed to thrive as an EEG Data Scientist, and why are they important?

To thrive as an EEG Data Scientist, you need a strong background in neuroscience or biomedical engineering, statistical analysis, and machine learning, often supported by advanced degrees in related fields. Proficiency in programming languages like Python or MATLAB, experience with EEG analysis software (e.g., EEGLAB or MNE), and familiarity with signal processing techniques are typically required. Strong analytical thinking, problem-solving skills, and effective communication are crucial soft skills for translating complex data into actionable insights. These skills enable accurate interpretation of EEG data, development of innovative solutions, and effective collaboration with interdisciplinary teams.

What are some common challenges faced by EEG Data Scientists when working with raw EEG data?

EEG Data Scientists often encounter challenges such as managing large volumes of noisy, artifact-laden data and ensuring accurate preprocessing before analysis. Handling physiological and environmental artifacts, such as eye blinks or muscle movements, requires specialized techniques to clean the signals. Additionally, developing robust algorithms for feature extraction and interpretation can be complex due to the variability between subjects. Collaboration with neuroscientists and clinicians is often essential to validate findings and ensure clinically meaningful results.
Infographic showing various Eeg Data Science job openings in the United States as of May 2026, with employment types broken down into 89% Full Time, 10% Part Time, and 1% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $71,720 per year, or $34.5 per hour.

Full-time

Posted 19 days ago


Job description

Job Summary:
Ceribell is a medical technology company focused on transforming the diagnosis and management of patients with serious neurological conditions. The successful candidate will join the data science team and will be responsible for the design and development of new state-of-the-art classification algorithms for EEG neurodiagnostics.
Responsibilities:
• Use time-series analysis, statistical signal processing and machine learning techniques to design classification algorithms for various neurological indications
• Analyze data for trends and patterns, and interpret data with a clear objective in mind
• Collaborate with business and clinical stakeholders to define project needs and communicate results of the models/analytical solutions designed
• Select and implement appropriate evaluation metrics to assess the performance of algorithms and models, considering both technical accuracy and clinical relevance.
• Document and validate models and perform statistical analysis to comply with regulatory requirements for medical device algorithms
• Mentor and guide junior data scientists and interns, fostering their growth by providing technical direction, feedback, and support
• Manage multiple projects independently, effectively prioritizing tasks and aligning deliverables with key milestones to ensure timely and successful outcomes
• Stay updated with the latest advancements in machine learning and neurodiagnostics to ensure the algorithms are state-of-the-art
Qualifications:
Required:
• PhD in Electrical Engineering, Computer Science, Statistics, or equivalent disciplines
• 7+ years of relevant research and/or industry experience in signal processing, filtering, statistical data analysis, time-series analysis, pattern recognition, feature engineering, machine learning and algorithm development
• Proficient in Python and/or Matlab and/or R or similar programming languages
• Experience and interest in biological signal processing and algorithm development for biomedical applications
• Strong analytical skills, detail-oriented and collaborative
• Experience with large-scale datasets, including preprocessing, cleaning, and handling noisy or imbalanced data in a biomedical context
• Proven ability to work independently, define scope and manage complex technical projects from concept to deployment
• Experience with advanced machine learning techniques, such as deep learning architectures (e.g., CNNs, RNNs) and their application to time-series data
• Strong preference for experience in neurodiagnostics or EEG data processing and algorithm development
Company:
Ceribell is a medical technology company focused on transforming the diagnosis and management of patients with serious neurological conditions. Founded in 2014, the company is headquartered in Mountain View, USA, with a team of 201-500 employees. The company is currently Growth Stage.