Summary:

This role is responsible for leveraging expertise in data analytics, machine learning, natural language processing, advanced statistical methods, and programming to derive insights from clinical data. As a member of the Enterprise Data Science team, this role will be the technical expert within specific projects and service lines, responsible for independently developing machine learning models and advanced statistical solutions to improve clinical outcomes and operational performance.

Responsibilities:

• Provide guidance and mentorship to junior team members, fostering their professional growth and development.
• Collaborate with cross-functional teams including clinical leaders, data scientists, and software engineers to identify data-driven opportunities for enhancing clinical processes and patient care.
• Utilize cloud-based technologies, such as Google Cloud Platform (GCP), for scalable data processing and analysis.
• Utilize Python programming and associated libraries such as TensorFlow, SciPy, PyTorch, Keras, Pandas and NumPY to create, train, test and implement traditional data science models.
• Utilize Natural Language Processing (NLP) programming models such as BERT, NLTK, spaCy and large language models to develop bidirectional text-based models to improve clinical and operational processes that are documentation-focused.
• Direct junior team members on necessary components of a clinically relevant and accurate cloud-based feature store
• Independently perform advanced analytics of clinical data and present findings to senior members of the CHS Clinical Operations leadership.
• Independently assess and select best model-data fit, and develop and implement machine learning models to predict high value clinical and operational outcomes to support day-to-day operations across multiple services across the organization.
• Collaborate with healthcare professionals and domain experts to understand clinical needs and design data-driven solutions.
• Design and conduct experiments, interpret results, and communicate findings to both technical and non-technical stakeholders

Qualifications:

• Ph.D. degree in Data Science, Computer Science, Bioinformatics, or a related field; or, Master’s degree and 2 years of direct experience in a cloud-based healthcare or healthcare adjacent field.
• Minimum 2 years of demonstrated experience of developing, operationalizing and deploying automated data models (statistical, machine learning or artificial intelligence) in a cloud based environment.
• Advanced knowledge of cloud computing, containerization technologies like Docker and Kubernetes, and experience deploying ML models to production environments.
• Proven experience in analyzing clinical data and implementing machine learning models such as Support Vector Machines, K nearest neighbors, random forests, gradient boosed decision trees, and naïve bayes.
• Proficiency in Python programming and associated libraries for supervised machine learning including Scikit-learn, Keras, Pandas, NumPy, PyTorch, TensorFlow and Seaborn.
• Demonstrated proficiency in the development and implementation of NLP-specific applications, include tools such as sci-spacy, BERT, NLTK, or Gemini.
• Demonstrated proficiency in analyzing clinical data and implementing machine learning models such as Support Vector Machines, K nearest neighbors, random forests, gradient boosted decision trees, and naïve bayes.
• Experience with cloud-based platforms such as Google Cloud Platform (GCP) for data storage, processing, and deployment.
• Strong problem-solving skills and ability to work independently and collaboratively in a fast-paced environment.
• Excellent communication and presentation skills with the ability to translate technical concepts to non-technical audiences.