Data Scientist

● Develop, train, and validate advanced machine learning and deep learning models (e.g., multi-layer perceptron's, convolutional neural networks, recurrent networks, and transformers) to predict patient risk, disease progression, and complex healthcare utilization patterns.Build and scale deep learning pipelines to process high-dimensional clinical datasets, medical imaging, or time-series physiological data● Formulate and execute predictive and prescriptive modeling strategies to optimize clinical workflows, inpatient flow, and resource allocation.● Apply rigorous statistical methods and causal inference techniques (e.g., propensity score matching, difference-in-differences) to isolate and evaluate the clinical and operational impact of new hospital programs and pathways.Translate complex machine learning, deep learning, and statistical outputs into intuitive, actionable insights for clinicians, care managers, and executive leadership Education● Bachelor’s degree in Statistics, Computer Science, Engineering, or a highly quantitative discipline. Master’s or PhD in Data Science, Computer Science, Deep Learning, Biostatistics, Biomedical Informatics, or a quantitative field with a heavy focus on machine learning andExperience● 1+ years of professional experience working strictly as a Data Scientist, Machine Learning Engineer, or Deep Learning Specialist.● Prior experience in healthcare, clinical informatics, hospital operations, or pharmaceutical environments is highly preferred but not mandatory.● Proven track record of developing, validating, and deploying predictive models (traditional ML or Deep Learning).● Experience working directly with large, complex datasets (e.g., unstructured text, times-series, claims, or clinical records).Tools and Technologies● Data Science, ML & Deep Learning: Python (pandas, scikit-learn, XGBoost, PyTorch, TensorFlow, Keras, Hugging Face).● BI & Model Visualization (Preferred): Streamlit, Plotly, Tableau, Power BI.● Cloud & Infrastructure (Familiarity): Azure ML, AWS SageMaker, Docker, Git. Education● Bachelor’s degree in Statistics, Computer Science, Engineering, or a highly quantitative discipline. Master’s or PhD in Data Science, Computer Science, Deep Learning, Biostatistics, Biomedical Informatics, or a quantitative field with a heavy focus on machine learning andExperience● 1+ years of professional experience working strictly as a Data Scientist, Machine Learning Engineer, or Deep Learning Specialist.● Prior experience in healthcare, clinical informatics, hospital operations, or pharmaceutical environments is highly preferred but not mandatory.● Proven track record of developing, validating, and deploying predictive models (traditional ML or Deep Learning).● Experience working directly with large, complex datasets (e.g., unstructured text, times-series, claims, or clinical records).Tools and Technologies● Data Science, ML & Deep Learning: Python (pandas, scikit-learn, XGBoost, PyTorch, TensorFlow, Keras, Hugging Face).● BI & Model Visualization (Preferred): Streamlit, Plotly, Tableau, Power BI.● Cloud & Infrastructure (Familiarity): Azure ML, AWS SageMaker, Docker, Git.