Role overview
As a Senior ML Engineer / Senior Data Scientist at CoMind, you will join a multidisciplinary team working at the intersection of neurophysiology, optics, machine learning, and signal processing. Your focus will be on analysing multidimensional time-series datasets collected by our next-generation neural sensor in both clinical trial and in-house experimental settings.
You will play a key role in interpreting physiological and optical signals to derive actionable insights that inform product development and clinical decision-making. Working closely with clinicians, neurophysiologists, physicists, and engineers, you will help lead algorithm and ML model development to extract meaningful metrics of brain function, improve signal quality through advanced denoising and demixing techniques, and validate signal fidelity in real-world use.
At CoMind, all team members work at least 4 days per week from our new Kings Cross offices, plus a flexible work-from-home day.
What you'll work on
- Lead the development and delivery of novel signal processing and machine learning models to interpret physiological signals, e.g. time series regression, classification and outliers detection
- Lead exploratory data analysis on complex time-series datasets generated from clinical trials, internal studies, and external research databases
- Design and validate algorithms for denoising, signal demixing, and interpretation of neuromonitoring data
- Write high-quality, well-tested Python code that meets industry standards.
- Collaborate with domain experts to translate clinical and physiological requirements into robust data analysis workflows
- Deliver signal processing methodologies ready for translation into medical device products
- Produce clear and insightful white papers, documentation, and visualisations for both technical and non-technical stakeholders
- Participate in research planning by gathering requirements, scoping work items, and contributing to roadmap discussions.
What we're looking for
- Proven track record in applying machine learning / deep learning techniques for time-series, physiological signals or continuous sensor data
- >4 years of experience in a research or applied data science role, ideally involving cross-disciplinary collaboration with clinical or experimental teams
- A deep understanding of digital signal processing concepts such as sampling and quantisation, spectral analysis, etc
- Fluent in Python and expert in industry-standard tools for version control, data engineering, CI/CD and reproducible research workflows
- Comfortable working in a fast-paced, research-driven environment with a strong sense of ownership and a willingness to learn and experiment
- Excellent written and verbal communication skills for conveying complex results clearly to technical and non-technical stakeholders.
- Experience with signal analysis and pipeline design for time series data in the context of wearable or medical products
- Hands-on experience in software engineering
- Familiarity with regulated development processes (e.g. SaMD)
- Strong understanding of neurological and cardiovascular signals
- Strong background in biostatistics
- Strong understanding of the physical principles associated with optical spectroscopy, interferometry, and related techniques.