Data Scientist II

Cohere Health

2d • $137,000 - $161,000 • Remote

About The Position

Data Scientist II at Cohere Health, Inc. in Boston, Massachusetts (Remote). This role involves gathering business requirements, applying trend analysis to identify actionable insights using analytic tools, and analyzing healthcare claims and authorization data to identify outliers, interpret patterns, and assess trends and opportunities. The position will also apply large language models (LLMs), stochastic optimization methods, and related technologies to support decision-making. Responsibilities include monitoring industry trends, regulatory changes, and emerging fraud schemes to support detection strategy development, maintaining methodology documentation, and presenting findings to leadership. Additionally, the role involves training, fine-tuning, and implementing LLMs and deep learning models for tasks such as anomaly detection, classification, and automated analysis of security-related or operational data. The Data Scientist II will analyze datasets to assess expected impacts and return on investment, including effects on medical expense, administrative cost, and clinical outcomes. They will develop data models, algorithms, and simulations to support analytical objectives and build visualizations and dashboards to communicate analytical findings and support business workflows. This is a fully remote position that can be performed from anywhere within the United States, with occasional travel to company headquarters in the U.S. for onboarding, team meetings, and company events.

Requirements

• 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.
• Three (3) years of experience in analyzing healthcare datasets, including EMR/EHR data, medical and pharmacy claims data, and Social Determinants of Health (SDoH) data.
• Three (3) years of experience in performing data quality analysis to identify data quality issues, gaps, or inconsistencies.
• Three (3) years of experience in 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.
• Three (3) years of experience in utilizing trend identification and data analysis methods such as regression analysis and outlier detection model in python, spark and sql.
• Three (3) years of experience in building and implementing models, creating algorithms, and running simulations.
• Three (3) years of experience in developing Tableau visualization and partnering with product owners and stakeholders to drive enterprise-level solutions that integrate data science into business workflows.
• Three (3) years of experience in developing NLP, LLMs (extractive and generative), 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 (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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level