Data Scientist II
New
C
Cohere HealthHealthcare analytics
This is a fully remote position and may be performed from anywhere within the United States.Full-TimeMiddle
Salary137,000 - 161,000 USD per year
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Job Details
- Experience
- 36 months of experience as Analyst or related position analyzing datasets; three (3) years of experience in the listed skills, which may be gained concurrently with the above experience.
- Required Skills
- PythonSQLPySpark
Requirements
- Have a Master’s degree in Data Science, Statistics, Biostatistics, or a related field; foreign education equivalent is accepted.
- Have 36 months of experience as an Analyst or in a related position analyzing datasets.
- Have three years of experience analyzing healthcare datasets, including EMR/EHR data, medical and pharmacy claims data, and Social Determinants of Health (SDoH) data.
- Have experience performing data quality analysis to identify data quality issues, gaps, or inconsistencies.
- Have experience building scalable AWS Airflow pipelines to ingest, integrate, and transform terabytes of data using Python, PySpark, and AWS S3 storage, and accelerating business decision-making in AWS Sagemaker.
- Have experience using trend identification and data analysis methods, including regression analysis and outlier detection models, in Python, Spark, and SQL.
- Have experience building and implementing models, creating algorithms, and running simulations.
- Have experience developing Tableau visualizations and partnering with product owners and stakeholders on enterprise-level solutions that integrate data science into business workflows.
- Have experience developing NLP and extractive and generative LLMs, including fine-tuning and LLM model development.
Responsibilities
- 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, 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 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, and build visualizations and dashboards to communicate analytical findings and support business workflows.
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