You:
- Excel at developing and deploying machine learning models to drive improved patient care and operational efficiency using complex healthcare data.
- Are skilled at creating and scaling LLM-based solutions, including Retriever-Augmented Generation (RAG) architectures, for innovative applications in healthcare analytics.
- Bring expertise in applying operations research techniques such as linear programming, queueing theory, and optimization to healthcare challenges like scheduling, resource allocation, and capacity planning.
- Collaborate effectively with clinicians, operations teams, and product managers to align data-driven solutions with business goals.
- Design experiments, pilot studies, and A/B tests to assess the impact of interventions in clinical and operational contexts.
- Stay current with advancements in machine learning, operations research, and healthcare analytics, ensuring innovative and compliant solutions.
- Prioritize adherence to healthcare privacy regulations (e.g., HIPAA) and implement secure workflows for data handling and deployment.
- Have excellent communication skills and can convey complex technical concepts to both technical and non-technical stakeholders.
Requirements:
- 5+ years of experience in data science, predictive analytics, or related roles.
- Exposure to EHR data structures and workflows in healthcare.
- Proficiency in Python or R, with experience in libraries/frameworks like scikit-learn, PyTorch, or TensorFlow.
- Strong SQL skills and familiarity with distributed computing platforms like Spark or Hadoop.
- Experience with cloud environments (AWS, GCP, Azure) and MLOps best practices.
- Knowledge of LLMs, NLP, and Retriever-Augmented Generation (RAG).
- Strong analytical thinking and problem-solving abilities.
- Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative discipline.
Nice to Haves:
- Hands-on experience with operations research methods, simulation modeling, and resource optimization in healthcare or related fields.
- Familiarity with RTLS data for asset tracking or workflow analysis.
- Previous experience mentoring or leading junior data scientists or engineers.
- Familiarity with DevOps/MLOps tools like Docker, Kubernetes, and Airflow, as well as CI/CD pipelines.
- Advanced knowledge of simulation modeling and its application in healthcare.
- Experience developing tools for real-time decision-making in healthcare environments.
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