Data Scientist

First Point Urgent Care

United States

United States

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Overview The Data Scientist applies statistical analysis, machine learning, numerical optimization, and data-driven modeling to extract insights from complex healthcare and research datasets. The role focuses on designing, validating, and deploying scalable analytical systems that support clinical decision making, operational strategy, and healthcare technology development. Responsibilities Analyze large healthcare, clinical, and operational datasets to identify trends, risks, and actionable insights. Apply statistical methods including regression, hypothesis testing, multivariate analysis, time series analysis, and forecasting. Formulate and solve modeling problems using numerical optimization techniques such as gradient-based methods, constrained optimization, and regularization. Evaluate optimization trade-offs including convergence, stability, and computational efficiency in model training and inference. Design, train, and evaluate predictive and machine learning models using Python, PyTorch, and supporting frameworks. Develop AI-driven systems to support clinical risk identification, patient triage, and care prioritization. Perform feature engineering, dimensionality reduction, and model selection using PCA, ICA, RFE, clustering, and ensemble methods. Build forecasting models for patient volume, no-show risk, and treatment outcomes. Implement data pipelines and analytical workflows using Python, SQL, and cloud-native tools on Linux environments. Package and deploy analytical and machine learning models using Docker for reproducibility and scalability. Support CI/CD workflows for data science and machine learning pipelines, including automated testing, model versioning, and controlled deployment. Deploy, monitor, and maintain models on AWS using services such as SageMaker, EC2, S3, and related infrastructure. Develop and operate analytical systems on Linux operating systems, including Ubuntu, for local development and production environments. Optimize performance-critical components using Rust or Rust-based libraries when appropriate for production systems. Document analytical methods, optimization assumptions, validation results, and model limitations for technical and non-technical stakeholders. Collaborate with clinicians, engineers, and researchers to integrate data science solutions into operational healthcare systems.
Requirements Master's degree in Statistics, Applied Mathematics, Data Science or a closely related quantitative field. Strong foundation in probability, statistical inference, and numerical optimization. Proficiency in Python and R for statistical analysis and machine learning. Experience with machine learning frameworks such as PyTorch and scikit-learn. Understanding of optimization methods used in machine learning, including loss functions, regularization, and iterative solvers. Experience building data pipelines and analytical workflows in Linux environments. Familiarity with Linux operating systems, including Ubuntu, for development and deployment. Familiarity with Docker and containerized deployment of data science systems. Exposure to CI/CD practices for analytics or machine learning pipelines. Experience using AWS for data storage, model training, deployment, and monitoring. Working knowledge of Rust or experience using Rust-based tools for performance optimization preferred. Strong communication skills with the ability to explain complex analytical and optimization concepts clearly.
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United States

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English

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