Tacit Data Scientist Intern at Tacit working on ML/research team to develop data processing tools and improve decoding algorithms. Design and analyze experiments using bio-signals and collaborate with hardware engineers.
Responsibilities
As a data scientist intern, you be joining the ML / research team to develop new tools and techniques for data processing, data analysis, and improving our technology stack. You’ll be responsible for designing, running and analyzing experiments using our system, interpreting results to inform future data collection and hardware design, and improving our decoding algorithms. This role will feature hands-on work with our basic signals coming off custom hardware all the way up to studying performance scaling across large numbers of people.
Design, implement, and test algorithms for data feature extraction from bio-signals for use in machine learning models.
Rapid iteration on experiments for evaluating new hardware prototypes, data collection improvements, and to gain understanding of key aspects of our system.
Develop analytical tools for visualization and establish data quality metrics.
Work with hardware and electrical engineers to improve the overall sensing stack.
Collaborate with ML team on data augmentation, multimodal training, synthetic data experiments for improved generalization
Qualification
Independent work ethicEnjoy fast moving startup environmentStrong candidates may haveExpertise with speech motor controlBasic fundamentals in ML
Required
Currently enrolled in (or recently graduated from) a PhD program in neuroscience, biomedical engineering, computer science, applied math, or related fields.
Effective communication and writing skills.
Ability to collaborate effectively across different teams and rapidly adapt to new fields.
Fluency in Python (and PyTorch is a plus).
Experience with dimensionality reduction techniques and time series data analysis.
Independent work ethic, flexibility, and resourcefulness.
Enjoy fast moving startup environment, excited to build independently.
Strong candidates may have
Familiarity with real-time human-machine interaction, ranging from automatic speech recognition to closed-loop neural interfaces.
Expertise with speech motor control, communication prostheses, and/or speech neuroscience.
Prior experience with consumer wearable devices.
Basic fundamentals in ML, ML infrastructure: experiment management tracking (e.g. Hydra, WandB) and AWS, Slurm.
Preferred
Familiarity with real-time human-machine interaction
Expertise with speech motor control, communication prostheses, and/or speech neuroscience
Prior experience with consumer wearable devices
Basic fundamentals in ML infrastructure such as experiment management tracking (Hydra, WandB) and AWS, Slurm
