Head Of Medical Device Machine Learning Team
Current- Manage a team of 4 to 6 data scientists developing algorithms to non-invasively predict blood biomarkers using novel silicon photonics wearable medical device and SWIR optical sensors.- Design ML lifecycle to manage data streams, feature engineering and storage, model inference, model monitoring, shadow testing, detect anomalies, and regenerate stale models.- Build ML Ops using Pulumi IaC, MetaFlow, MLFlow, DVC, AWS services (Firehose, Lambda, API, TimeStream, Batch, S3, DynamoDB, CloudWatch, Step Fn), and write Python SDKs to manage data and pipelines.- Lead ML development in human studies. Collect high frequency timeseries data, perform exploratory data analysis and data visualization, signal processing and feature engineering, train models using PyTorch, XGBoost, and Scikit Learn and continuously deploy into cloud.- Management responsibilities include developing ML R&D roadmap, performance evaluations, technical mentorship, managing conflicts, project timelines, and risks.- Collaborate with domain experts and stakeholders from large MedTech companies, consumer health companies, and research organizations. Cross-functional collaboration with business teams to develop product and engineering requirements.- Document software and algorithms in accordance to IEC 62304, ISO 13485, ISO 9000, and ISO 26262 to meet standards for the medical device industry.
