oracle
Data Scientist - Statistics/ML - Remote
Molina Healthcare
USA
Onsite
Discovered Yesterday
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Perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy.
Execute data science and statistical analytical experiments methodically to help solve various problems and make a true impact across various healthcare domains.
Developing and deploying advanced machine learning models and AI solutions that enhance our products and services.
Leverage their expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions.
Full Job Posting
Job Summary
Perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy.
Execute data science and statistical analytical experiments methodically to help solve various problems and make a true impact across various healthcare domains.
Developing and deploying advanced machine learning models and AI solutions that enhance our products and services.
Leverage their expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions.
Job Duties
- Data Analysis and Interpretation: Extract meaningful insights from complex datasets, identify patterns, and interpret data to inform strategic decision-making.
- Machine Learning Model Development: Design, develop, and train machine learning models using a variety of algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
- Agentic Workflows Implementation: Develop and implement agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities.
- RAG Pattern Utilization: Employ retrieval-augmented generation patterns to improve the performance of language models, ensuring they can access and utilize external knowledge effectively to enhance their outputs.
- Model Fine-Tuning: Fine-tune pre-trained models to adapt them to specific tasks or datasets, ensuring optimal performance and relevance in various applications.
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