Data Scientist II

Duties:

Gather business requirements and apply trend analysis to identify actionable insights using analytic tools. Analyze healthcare claims and authorization data to identify outliers, interpret patterns, and assess trends and opportunities. Apply large language models (LLMs), stochastic optimization methods, and related technologies to support decision-making. Monitor industry trends, regulatory changes, and emerging fraud schemes to support detection strategy development, maintain methodology documentation, and present findings to leadership. Train, fine-tune, and implement LLMs and deep learning models for tasks such as anomaly detection, classification, and automated analysis of security-related or operational data. Analyze datasets to assess expected impacts and return on investment, including effects on medical expense, administrative cost, and clinical outcomes. Develop data models, algorithms, and simulations to support analytical objectives. Build visualizations and dashboards to communicate analytical findings and support business workflows.

Requirements:

Must have a Master’s degree in Data Science, Statistics, Biostatistics or related field (willing to accept foreign education equivalent) and 36 months of experience as Analyst or related position analyzing datasets. Must possess three (3) years of experience (may be gained concurrently with above experience) in the following skills:

• Analyzing healthcare datasets, including EMR/EHR data, medical and pharmacy claims data, and Social Determinants of Health (SDoH) data;
• Performing data quality analysis to identify data quality issues, gaps, or inconsistencies;
• Building scalable AWS Airflow pipelines to ingest, integrate, and transform terabytes of data using Python and PySpark and AWS S3 storage, and accelerate business decision-making in AWS Sagemaker;
• Utilizing trend identification and data analysis methods such as regression analysis and outlier detection model in python, spark and sql.;
• Building and implementing models, creating algorithms, and running simulations;
• Developing Tableau visualization and partnering with product owners and stakeholders to drive enterprise-level solutions that integrate data science into business workflows;
• Developing NLP, LLMs (extractive and generative), fine-tuning, and LLM model development.