Data Scientist, Biosignal Deep Learning

Naqi Logix Inc. is a neural technology company developing a non-invasive human-machine interface platform that delivers advanced command and control capabilities through wearable devices. Its core solution is deployed via smart neural earbuds, designed to resemble and function like everyday consumer earbuds. The technology is entirely external, residing in the user’s ear, and does not require brain or body implants or invasive procedures.

Users can insert the earbuds when needed, activate or deactivate them at will, and choose when to wear or not wear the device. This flexible approach enables wider adoption of neurotechnology in practical, user-friendly applications.

The Data Scientist, Biosignal Deep Learning role is a full-time, remote role that is Canada based. This role focuses on building and refining deep learning models to interpret biosignals collected from Naqi’s neural earbuds, transforming raw data into reliable, real-time control signals. Day-to-day responsibilities include designing experiments, cleaning and preprocessing biosignal datasets, performing statistical and exploratory data analyses, and developing and evaluating machine learning pipelines.

The Data Scientist will collaborate closely with engineering, product, and research teams to integrate models into production systems, optimize performance, and ensure robustness across diverse users and environments. Additional activities include documenting methodologies, presenting findings to cross-functional stakeholders, and staying current with advances in biosignal processing, neural interfaces, and applied deep learning.

• Strong foundations in Data Science and Statistics, with experience applying statistical methods to biosignal or time-series data.
• Proficiency in Data Analysis and Data Analytics, including data cleaning, feature engineering, and exploratory analysis for complex sensor datasets.
• Ability to create clear and informative Data Visualization outputs to communicate model performance, insights, and experimental results.
• Hands‑on experience with deep learning frameworks (e.g., PyTorch, Tensor Flow) and models for biosignal, EEG, EMG, or related neural interface data.
• Proficiency in Python or a similar programming language, including use of scientific and machine learning libraries (e.g., Num Py, pandas, scikit‑learn).
• Background in a quantitative field such as Computer Science, Electrical Engineering, Biomedical Engineering, Mathematics, or related discipline; advanced degree is a plus.
• Experience deploying models to production or working closely with engineering teams on real‑time systems and embedded or edge devices is beneficial.
• Ability to work on‑site in Los Angeles, CA, collaborate effectively in a multidisciplinary environment, and communicate technical concepts to non‑specialists.

• 2–4 years of hands‑on deep learning experience in industry or applied research.
• At least one model you have taken past the prototype stage into a product, a device, or a validated research result.
• Strong Python and deep fluency with PyTorch and/or Tensor Flow/Keras.
• Solid experience with time‑series or signal data, and working knowledge of digital signal processing (sampling, filtering, spectral analysis).
• A background in computer vision or another deep learning field, and the ability to apply it to sequential and sensor data.
• Experience fitting models to resource limits on mobile, embedded, or edge targets.
• Regular, practical use of AI coding assistants or agentic tools such as Claude Code in your work.
• Sound instincts about evaluation. You are suspicious of results that look too good, and you know how to check them.
• Strong habits with Git, code review, and collaborative…