Sr. Data Scientist - Healthcare AI & Analytics

View full description

Requirements:

• Advanced Degree (Master’s or Ph.D.) in a quantitative field
• 5+ years of experience in data science, with a focus on healthcare analytics and NLP.
• Proficiency in Python, Jupyter Notebooks, and Python libraries like Pandas, Numpy, and Scikit-learn.
• Advanced data engineering skills using SQL, GIT, and Bitbucket
• Strong background in machine learning, deep learning, LLM applications, and NLP techniques.
• Experience with cloud services: AWS: Bedrock, SageMaker, Comprehend Medical, Lambda, S3; Azure: Machine Learning, Cognitive Services, Azure Functions; GCP: Vertex AI, Cloud Natural Language API, Cloud Functions
• Experience using and implementing RAG systems and AI agents.
• Familiarity with healthcare data standards (HL7, FHIR) and regulatory requirements (HIPAA).
• Strong communication skills for presenting complex findings to various stakeholders.

Responsibilities:

• Collaborate with product teams and clients to translate real-world healthcare issues into well-defined problem statements and data science solutions.
• Select appropriate datasets, process and cleanse data for analysis, and develop data structures to organize, collect, and standardize data.
• Develop influential features using machine learning and AI techniques to enhance model development.
• Build and develop ML/AI models to support our expanding portfolio of healthcare-centric solutions, integrating GenAI and LLMs to enhance data models and algorithms with a focus on natural language processing tasks.
• Oversee deployment of GenAI and LLM models, ensuring robust monitoring, maintenance, and updates to enhance model performance.
• Document project development, including problem definitions, data processing, model deployment, and results for auditability.
• Build presentations, dashboards, and reports to communicate analytical insights effectively to various stakeholders.
• Serve as a GenAI expert and provide mentorship, peer review, and guidance to other team members.
• Develop and deploy AI agents for healthcare analytics, enabling autonomous decision-making.
• Utilize cloud platforms (AWS, Azure, GCP) to build scalable data analytics pipelines and AI models.
• Apply NLP techniques to extract insights from unstructured healthcare data such as clinical notes and medical literature. Leverage generative AI and NLP techniques to develop predictive models and extract actionable insights from healthcare data.
• Implement text classification, named entity recognition, and sentiment analysis models for healthcare applications across multiple cloud platforms.
• Develop and utilize unit tests to ensure the functional correctness of models.
• Collaborate with cross-functional teams to integrate AI/ML products into existing solutions and workflows.
• Perform exploratory data analysis on high-dimensional datasets, both structured and unstructured.
• Implement text classification, named entity recognition, and sentiment analysis models for healthcare use cases.