Boston Scientific's Active Implantable Systems (AIS) group is looking to innovate and create a paradigm shift to solve unmet clinical needs and change what is possible for patients by developing implantable devices that monitor, support diagnosis, and treat irregular heart rhythms, heart failure, and sudden cardiac arrest. This role aims to leverage data science and AI/ML to enhance device performance and patient outcomes.
Requirements
- Experience using programming languages such as Python, Kotlin, or Java (Python used in this role)
- Experience with AI/ML and deep learning-based prediction model development
- Experience with testing-related projects in school or work
- Experience working with/studying Electrocardiogram (ECG) and physiological signals using signal processing
Responsibilities
- Develop deep learning models to predict cardiac events using ECG and other physiological signals
- Apply signal processing techniques to clean, analyze, and interpret biosignals for device diagnostics and monitoring
- Support the design of AI/ML algorithms that enhance implantable device performance and patient outcomes
- Collaborate with engineers to integrate Python-based tools for data analysis, simulation, and model validation
- Contribute to feasibility studies exploring novel diagnostic features for heart rhythm and heart failure detection
- Analyze large datasets from implantable devices to identify patterns and inform next-generation therapy development
- Work closely with clinical, software, and hardware teams to ensure alignment between algorithm development and device capabilities
Other
- Masters or Ph.D. student. Must have at least one semester of school left post-internship to qualify.
- Working towards a degree in Biomedical Engineering (EE emphasis), Electrical Engineering, or Data Science.
- Must be able to start internship on May 18th or 26th, 2026 and work for 12 weeks
- Must have reliable transportation to/from work.
- Medical device industry experience
- Excellent communication and collaboration skills
