Data Scientist / Machine Learning Engineer
iRhythm Technologies, Inc. is a San Francisco-based medical device company combining novel diagnostic device and data analysis concepts together to create a new approach to cardiac rhythm monitoring. We are looking for a data scientist / machine learning engineer to help develop the next generation of our algorithms and play a key role in our data science initiatives. The successful candidate will have the opportunity to join a new team applying data-driven machine learning technologies to large data sets from wearable medical sensors.
- Design, build and test machine learning algorithms for the analysis of electrocardiogram (ECG) signal data
- Perform exploratory data analysis and visualizations of multivariate clinical data sets
- Work closely with our software engineering team and clinical experts to deploy machine learning algorithms into production
- MS or PhD in Computer Science, Electrical Engineering, Statistics or a related quantitative field
- Excellent knowledge of various machine learning classification algorithms, statistical modeling and time-series analysis
- Basic knowledge of signal processing concepts like filtering, Fourier and wavelet transforms
- Strong programming skills in Python and familiarity with python libraries like numpy, scikit-learn, pandas etc.
- Excellent knowledge of data structures, algorithms and modern OOP techniques
- Experience working with real-world data sets with possible noise in data and labels
- Ability to work independently and meet deadlines
- Strong interpersonal and verbal communication skills
- Previous research experience with biomedical signals
- Familiarity or experience with recent advances in deep neural network architectures and software frameworks like Theano and TensorFlow
- Experience developing software in a Unix/Linux programming environment
- Familiarity with cloud computing and big data technologies
FLSA Status Exempt
iRhythm Technologies, Inc. is an Equal Opportunity Employer (M/F/V/D).
San Francisco, CA 94103