Data Scientist, Statistical Signal Processing and Learning

Onsite/Hybrid/Remote /
Data Science | Algorithms /
Onsite/Hybrid/Remote | Permanent | Full-time
Kernel is bringing neuro measurement mainstream. We have built the next generation non-invasive brain interface. From the photon through machine learning, we are a close-knit, full-stack team that thrives on pushing the limits. Join us on this epic journey as we strive to usher in a new era of understanding ourselves, each other and the future of our shared existence.

We are looking for a Data Scientist to support the processing, analysis, visualization, interpretation, and application of the signals captured by our new neuroimaging technology, Flow. The candidate should be passionate about building advanced algorithms for performing inference on complex neural and physiological signals. The ideal candidate has a demonstrated proficiency for writing clean, modular code and a passion for continually developing their data science and engineering skills. They will work closely with a team of scientists and engineers in a rich and nurturing environment, and are expected to have agility and self-direction to adapt to the evolving and complex challenges that we are tackling.


    • Design and implement signal processing and analysis techniques to extract maximal signal from raw time-domain fNIRS data
    • Communicate progress, challenges, and results effectively to an interdisciplinary team of neuroscientists, physicists, data scientists, and software engineers


    • Experience in statistical signal processing, time series analysis, time-frequency analysis methodologies
    • Demonstrated ability to write clean, efficient, and well-documented code in Python
    • Experience with numerical Python (numpy, scipy) and plotting (matplotlib, plotly) libraries
    • Experience with collaborative software development including version control and code reviews


    • Experience with hemodynamic signals (fMRI, fNIRS)
    • Experience with state of the art big data tools for parallel processing, analysis, and machine learning
    • Peer-reviewed publications in data science, machine learning, or neuroscience