With Collective Medical & Audacious Inquiry, we’ve become the most expansive, full-continuum care collaboration network, offering care teams immediate, point-of-care access to deep, real-time insights at every stage of a patient’s journey.
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Location: US (Remote)
As a Senior Applied Researcher – Machine Learning within the PointClickCare Advanced Technology team, you will work with customers, product leaders, subject matter experts, other highly experienced Applied Researchers, engineers, or others on cutting-edge research and development projects to address the needs of users in the acute, post-acute, long term and cross-continuum care space to truly transform healthcare.
- Perform research, experimentation, and related engineering to design, build and evaluate machine learning and NLP-based models to be deployed into production environments,
- Tasks may include (for example) data collection, data cleaning, data analysis, model training, development, and evaluation, and scaling up the system.
- Master’s degree or equivalent experience in Computer Science, Math, Physics, Engineering or a related field and experience with advanced mathematics or statistical methods applicable to machine learning and NLP
- Proven industry experience, through multiple major product releases
- Proficiency in Python
- Experience doing data engineering for ML and NLP applications, including exposure to database systems and proficiency with SQL.
- Exposure to building models from big data using modern machine learning and NLP packages and data analysis stacks such as NumPy, SciPy, Scikit-learn, Pandas, Keras, Tensorflow, PyTorch, CNTK or NLTK, and experience such as fine tuning LLMs or other Transformer based models
- Curious problem finder and problem solver, able to think both creatively and methodically.
- Strong interest in applying machine learning and NLP to healthcare-related problems and data.
- Works well with others and independently and comfortable working on a distributed team
- Experience working with large data sets using big data processing frameworks (e.g. Azure Data Lake, HDFS/Hadoop, Spark or other cluster computing/MapReduce frameworks) and/or public cloud infrastructure (Azure, AWS, Google Cloud) for building, evaluating, or deploying machine learning and NLP models, including experience with end to end SaaS-scale ML/NLP pipelines, is a plus.
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