Description

You will apply advanced statistical methods and machine learning to clinical and insurance datasets.

Responsibilities

• Collect, clean, and preprocess clinical data from health insurance claims and electronic health records (EHRs).
• Integrate and manage large-scale datasets from sources such as CPRD and claims databases.
• Develop and validate predictive models using regression, classification, clustering, and ensemble methods.
• Map clinical data using medical coding systems including ICD-9, ICD-10, and SNOMED CT.
• Create visualizations and reports to communicate complex findings to technical and non-technical stakeholders.

Required Skills

• 5+ years of experience in data science or a related field.
• Proficiency in Python or R for data manipulation and model development.
• Strong SQL knowledge for data extraction and management.
• Experience with machine learning frameworks such as scikit-learn and TensorFlow.
• Knowledge of Federated Machine Learning techniques.
• Expertise in pandas for data analysis.
• Experience working with medical coding systems like ICD-9, ICD-10, and SNOMED CT.
• Ability to use visualization tools including Tableau, Matplotlib, or Seaborn.
• Bachelor's degree in a relevant field.

Key Skills

Education

Bachelor's degree