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CoMind is developing non-invasive neuromonitoring technology to transform clinical brain monitoring and improve diagnosis and treatment of brain disorders. The Data Science team develops algorithms and machine learning systems that convert raw optical interference signals into continuous, clinically meaningful measurements of cerebral blood flow, intracranial pressure, and autoregulation.
As Principal Data Scientist, you will serve as the technical authority for CoMind's data science function, setting methodological direction for signal conditioning and extraction, and personally driving the most complex, highest-impact problems. This is a senior individual contributor role—you will not manage people directly, but your technical leadership will shape how the entire team approaches its most challenging problems. You will work closely with R&D, Clinical, and Software Engineering teams.
Key responsibilities include:
- Act as technical authority for the end-to-end signal processing and inference chain, from raw optical data through to clinical measurements
- Set methodological direction for estimation, signal extraction, and ML modeling across the team
- Own signal processing and analysis programs from research through validated, production-quality implementations
- Derive achievable performance bounds from instrument and noise models to drive algorithm selection and design trade-offs
- Own how the team handles uncertainty and calibration, including inference methods for small clinical cohorts and per-patient estimation with quantified uncertainty
- Set standards for data science function operations: reproducibility, code review, experiment design, dataset governance, and ground-truth definitions
- Serve as primary technical reviewer and expert resource across the research organization
- Identify and introduce new best-practice methods from estimation, inference, and measurement science
- Mentor members of the Data Science team and wider business
- Contribute to CoMind's IP and publication strategy, authoring and reviewing technical papers, patents, and regulatory documents
The role is based in London with a hybrid arrangement: all team members work at least 4 days per week from the Kings Cross offices, plus one flexible work-from-home day per week.
REQUIREMENTS:
- 15+ years of experience in data science, ML, or applied research with a strong track record of independent technical leadership on complex, ambiguous problems
- Career experience extracting small signals from noisy, complex physiological systems and developing and deploying physical models to support those efforts
- Deep expertise in physiological time-series signal processing methodologies
- Demonstrated ability to take algorithmic work from research concept through to production-quality, validated implementations in shipped product
- Experience designing device performance evaluation methods where no prior gold-standard exists: simulating physical systems, running sensitivity analysis, identifying failure modes, and developing signal processing solutions
- Extensive experience owning calibration and cross-instrument comparability work involving noise models, error budgets, and evaluation of experimental data
- Strong record of scientific output: publications, patents, or equivalent evidence of original technical contribution and domain authority
NICE TO HAVE:
- Background in neuroscience, neuromonitoring, or cerebrovascular physiology
- Experience with optical signal processing (e.g., LiDAR, pulse oximetry, NIRS, laser doppler flowmetry, or related technologies)
- Experience in regulated industry (medical devices, diagnostics, pharma) with familiarity of algorithm validation and documentation requirements for software as a medical device
