Molina Healthcare
Principal Data Scientist - Generative AI, Machine Learning, Python, R - Remote
Molina Healthcare, Santa Fe, New Mexico, us, 87503
Overview
Responsible for overseeing data science projects, managing and mentoring a team, and aligning data initiatives with business goals. Lead the development and implementation of data models, collaborate with cross-functional teams, and stay updated on industry trends. Ensure ethical data use and communicate complex technical concepts to non-technical stakeholders. Lead initiatives on model governance and model ops to align with regulatory and security requirements. This role requires technical expertise, strategic thinking, and leadership to drive data-driven decision-making within the organization and be the pioneer on generative AI healthcare solutions, aimed at revolutionizing healthcare operations as well as enhancing member experience. Responsibilities
Research and Development: Stay current with the latest advancements in AI and machine learning and apply these insights to improve existing models and develop new methodologies. AI Model Deployment, Monitoring & Model Governance: Deploy AI models into production environments, monitor their performance, and adjust as necessary to maintain accuracy and effectiveness and meet all governance and regulatory requirements. Innovation Projects: Lead pilot projects to test and implement new AI technologies within the organization. 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. Data Cleaning and Preprocessing: Prepare data for analysis by performing data cleaning, handling missing values, and removing outliers to ensure high-quality inputs for modeling. Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes. Documentation and Reporting: Create comprehensive documentation of models, methodologies, and results; communicate findings clearly to non-technical stakeholders. Mentorship: Mentor, coach, and provide guidance to newer data scientists. Partnerships: Partner closely with business and other technology teams to build ML models which help in improving star ratings, reduce care gaps and other business objectives. Communication: Present complex analytical information to all levels of audiences in a clear and concise manner; collaborate with analytics team to assign and manage delivery of analytical projects as appropriate. Other Duties: Perform other duties as business requirements change, identify data solutions and technology-enabled solution opportunities and refer to the appropriate team members for building out payment integrity solutions. Industry Insight: Use a broad range of tools and techniques to extract insights from current industry or sector trends. Qualifications
Required Education:
Master’s Degree in Computer Science, Data Science, Statistics, or a related field. Required Experience/Knowledge, Skills & Abilities:
10+ years’ work experience as a data scientist, preferably in healthcare; candidates in other industries considered. Knowledge of big data technologies (e.g., Hadoop, Spark); familiarity with relational database concepts and SDLC concepts; strong critical thinking. Technical Proficiency:
Strong programming skills in Python and R; experience with TensorFlow, Keras, or PyTorch. Statistical Analysis:
Understanding of statistical methods and ML algorithms, including k-NN, Naive Bayes, SVM, and neural networks. Experience with Agentic Workflows:
Designing and implementing agentic workflows that leverage AI agents for autonomous operations. RAG Techniques:
Knowledge of retrieval-augmented generation techniques. Model Fine-Tuning Expertise:
Proven experience in fine-tuning models for specific tasks. Data Visualization:
Proficiency with Tableau, Power BI, or similar tools. Database Management:
Experience with SQL/NoSQL, data warehousing, and ETL. Problem-Solving Skills:
Strong analytical abilities and innovative thinking. Preferred Education
PHD or additional experience. Preferred Experience
Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio) for AI workflows and model deployment. Familiarity with NLP and computer vision techniques. EEO Statement:
Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V. Pay Range:
$117,731 - $275,491 / ANNUAL. Actual compensation may vary based on location, experience, education and/or skill level.
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Responsible for overseeing data science projects, managing and mentoring a team, and aligning data initiatives with business goals. Lead the development and implementation of data models, collaborate with cross-functional teams, and stay updated on industry trends. Ensure ethical data use and communicate complex technical concepts to non-technical stakeholders. Lead initiatives on model governance and model ops to align with regulatory and security requirements. This role requires technical expertise, strategic thinking, and leadership to drive data-driven decision-making within the organization and be the pioneer on generative AI healthcare solutions, aimed at revolutionizing healthcare operations as well as enhancing member experience. Responsibilities
Research and Development: Stay current with the latest advancements in AI and machine learning and apply these insights to improve existing models and develop new methodologies. AI Model Deployment, Monitoring & Model Governance: Deploy AI models into production environments, monitor their performance, and adjust as necessary to maintain accuracy and effectiveness and meet all governance and regulatory requirements. Innovation Projects: Lead pilot projects to test and implement new AI technologies within the organization. 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. Data Cleaning and Preprocessing: Prepare data for analysis by performing data cleaning, handling missing values, and removing outliers to ensure high-quality inputs for modeling. Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes. Documentation and Reporting: Create comprehensive documentation of models, methodologies, and results; communicate findings clearly to non-technical stakeholders. Mentorship: Mentor, coach, and provide guidance to newer data scientists. Partnerships: Partner closely with business and other technology teams to build ML models which help in improving star ratings, reduce care gaps and other business objectives. Communication: Present complex analytical information to all levels of audiences in a clear and concise manner; collaborate with analytics team to assign and manage delivery of analytical projects as appropriate. Other Duties: Perform other duties as business requirements change, identify data solutions and technology-enabled solution opportunities and refer to the appropriate team members for building out payment integrity solutions. Industry Insight: Use a broad range of tools and techniques to extract insights from current industry or sector trends. Qualifications
Required Education:
Master’s Degree in Computer Science, Data Science, Statistics, or a related field. Required Experience/Knowledge, Skills & Abilities:
10+ years’ work experience as a data scientist, preferably in healthcare; candidates in other industries considered. Knowledge of big data technologies (e.g., Hadoop, Spark); familiarity with relational database concepts and SDLC concepts; strong critical thinking. Technical Proficiency:
Strong programming skills in Python and R; experience with TensorFlow, Keras, or PyTorch. Statistical Analysis:
Understanding of statistical methods and ML algorithms, including k-NN, Naive Bayes, SVM, and neural networks. Experience with Agentic Workflows:
Designing and implementing agentic workflows that leverage AI agents for autonomous operations. RAG Techniques:
Knowledge of retrieval-augmented generation techniques. Model Fine-Tuning Expertise:
Proven experience in fine-tuning models for specific tasks. Data Visualization:
Proficiency with Tableau, Power BI, or similar tools. Database Management:
Experience with SQL/NoSQL, data warehousing, and ETL. Problem-Solving Skills:
Strong analytical abilities and innovative thinking. Preferred Education
PHD or additional experience. Preferred Experience
Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio) for AI workflows and model deployment. Familiarity with NLP and computer vision techniques. EEO Statement:
Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V. Pay Range:
$117,731 - $275,491 / ANNUAL. Actual compensation may vary based on location, experience, education and/or skill level.
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