Remote Data Scientist

Required Skills • Strong proficiency in Python for data science and ML development. • Hands-on experience deploying and scaling ML models in AWS (SageMaker, Lambda, EC2). • Prior experience working in healthcare or hospital environments. • Must be self-motivated and capable of managing tasks independently in a distributed team setting. • Bachelor’s or master’s in data science, Computer Science, or related field. Desired Skills (not Required But The More The Better) • Hands-on experience deploying and scaling ML models in Azure (Azure ML, Data Factory). • Familiarity with cloud-native ML workflows and containerization (e.g., Docker, Kubernetes). • Experience with ML libraries such as scikit-learn, TensorFlow, PyTorch. • Comfortable working with version control (Git) and collaborative development tools. • Understanding of HIPAA compliance and data privacy best practices in ML applications. • AWS and/or Azure certifications are a plus (e.g., AWS ML Specialty, Azure Data Scientist Associate). Day To Day Responsibilities • Collaborate with internal stakeholders (clinical, operational, and business teams) to understand data-related requests and projects goals. • Translate business and clinical questions into data science problems. • Access and extract relevant clinical datasets from internal systems and databases. • Clean, normalize, and structure data to create controlled datasets suitable for modeling. • Apply statistical and machine learning techniques using Python and AWS SageMaker to analyze clinical data. • Identify and validate predictor variables that influence patient outcomes or care decisions. • Develop algorithms based on model outputs to support hypothesis generation by providers and clinicians. • Ensure algorithms are interpretable, clinically relevant, and aligned with healthcare standards. • Work closely with app developers to integrate predictive models and algorithms into clinical applications. • Maintain thorough documentation of data sources, modeling processes, and algorithm logic. • Ensure all work complies with HIPAA and internal data governance policies.