Data Scientist

Key Responsibilities:

• Design and implement predictive models and machine learning algorithms to solve healthcare-specific challenges

• Analyze large, complex healthcare datasets including electronic health records (EHR) and claims data

• Develop statistical models for patient risk stratification, treatment optimization, population health management, and revenue cycle optimization

• Build models for clinical decision support, patient outcome prediction, care quality improvement, and revenue cycle optimization

• Create and maintain automated data pipelines for real-time analytics and reporting

• Work with healthcare data standards (HL7 FHIR, ICD-10, CPT, SNOMED CT) and ensure regulatory compliance

• Develop and deploy models in cloud environments while creating visualizations for stakeholders • Present findings and recommendations to cross-functional teams including clinicians, product managers, and executives

Qualifications required:

• Bachelor's degree in data science, Statistics, Computer Science, Mathematics, or related quantitative field

• At least 2 years of hands-on experience in data science, analytics, or machine learning roles

• Demonstrated experience working with large datasets and statistical modeling

• Proficiency in Python or R for data analysis and machine learning

• Experience with SQL and database management systems

• Knowledge of machine learning frameworks such as scikit-learn, TensorFlow, PyTorch

• Familiarity with data visualization tools such as Tableau, Power BI, matplotlib, ggplot2

• Experience with version control systems (Git) and collaborative development practices

• Strong foundation in statistics, hypothesis testing, and experimental design

• Experience with supervised and unsupervised learning techniques

• Knowledge of data preprocessing, feature engineering, and model validation

• Understanding of A/B testing and causal inference methods.

What You’ll Need to Be Successful (Required Skills):

Large Language Model (LLM) Experience:

Generative AI Frameworks:

Prompt Engineering and Optimization:

Vector Databases and Embeddings:

AI Model Evaluation:

• Multi-modal AI Systems: Familiarity with multi-modal generative models combining text, images, and other data types, including experience with vision-language models and cross-modal applications

AI Safety and Alignment: