Molina Healthcare
Lead Principal Data Scientist - Generative AI and Machine Learning
Molina Healthcare, Santa Fe, New Mexico, us, 87503
Overview
Join our dynamic team as a Lead Principal Data Scientist, where you will be at the forefront of generative AI healthcare solutions. This role involves leading data science projects, mentoring a talented team, and aligning data initiatives with our strategic business goals. You will drive the development and implementation of cutting-edge data models while collaborating with cross-functional teams, all while ensuring ethical data practices. Your leadership will be crucial in shaping data-driven decisions to enhance both healthcare operations and member experiences.
Responsibilities
Research and Development:
Stay updated on AI and machine learning advancements and leverage these insights to refine our existing models and create innovative methodologies. AI Model Deployment and Governance:
Oversee the deployment of AI models, monitor their performance, and make adjustments as needed to ensure regulatory compliance and model effectiveness. Innovation Projects:
Spearhead pilot projects that test and implement new AI technologies within our organization. Data Analysis:
Extract actionable insights from complex datasets and interpret data to guide strategic decisions. Machine Learning Model Development:
Design, develop, and train various machine learning models, utilizing a mix of supervised and unsupervised learning, deep learning, and reinforcement learning techniques. Implement Agentic Workflows:
Create and implement workflows that enable AI agents to execute tasks autonomously, improving operational efficiency. Utilize RAG Patterns:
Use retrieval-augmented generation patterns to enhance language model performance and ensure effective knowledge access. Model Fine-Tuning:
Adapt pre-trained models for specific tasks to achieve optimal performance and relevance. Data Preparation:
Conduct data cleaning and preprocessing to ensure high-quality inputs for modeling. Collaboration:
Work alongside software engineers, product managers, and business analysts to seamlessly integrate AI solutions into existing systems. Documentation:
Produce thorough documentation of methodologies and results and communicate findings to non-technical stakeholders. Mentorship:
Provide guidance and mentorship to junior data scientists, fostering their professional growth. Partnerships:
Collaborate closely with business and technology teams to develop ML models that enhance performance metrics like star ratings and care gap reduction. Effective Communication:
Present complex analytical concepts clearly to diverse audiences and manage the delivery of analytical projects. Industry Insights:
Utilize various tools and techniques to derive insights from current industry trends. Qualifications Required Education:
Master's Degree in Computer Science, Data Science, Statistics, or a related field. Experience:
10+ years as a data scientist, preferably with healthcare experience; candidates from other industries will also be considered. Familiarity with big data technologies, relational databases, and SDLC concepts is essential. Technical Skills:
Proficient in Python and R, with experience in TensorFlow, Keras, or PyTorch. Statistical Analysis:
Strong understanding of statistical methods and ML algorithms, including k-NN, Naive Bayes, and SVM. Agentic Workflows:
Experience designing and implementing workflows that use AI agents for autonomous operations. RAG Knowledge:
Familiar with retrieval-augmented generation techniques. Model Fine-Tuning:
Proven track record of successfully fine-tuning models for specific applications. Data Visualization:
Skilled in tools like Tableau or Power BI. Database Management:
Proficiency in SQL/NoSQL, data warehousing, and ETL processes. Problem-Solving:
Excellent analytical skills and innovative thinking. Preferred Education: PhD or equivalent experience is advantageous. Preferred Experience: Experience with cloud platforms such as Databricks, Snowflake, or Azure AI Studio. Familiarity with NLP and computer vision techniques. EOE Statement:
We are an Equal Opportunity Employer M/F/D/V. Pay Range:
$117,731 - $275,491 annually. Actual compensation will be determined based on location, experience, education, and skill level.
Stay updated on AI and machine learning advancements and leverage these insights to refine our existing models and create innovative methodologies. AI Model Deployment and Governance:
Oversee the deployment of AI models, monitor their performance, and make adjustments as needed to ensure regulatory compliance and model effectiveness. Innovation Projects:
Spearhead pilot projects that test and implement new AI technologies within our organization. Data Analysis:
Extract actionable insights from complex datasets and interpret data to guide strategic decisions. Machine Learning Model Development:
Design, develop, and train various machine learning models, utilizing a mix of supervised and unsupervised learning, deep learning, and reinforcement learning techniques. Implement Agentic Workflows:
Create and implement workflows that enable AI agents to execute tasks autonomously, improving operational efficiency. Utilize RAG Patterns:
Use retrieval-augmented generation patterns to enhance language model performance and ensure effective knowledge access. Model Fine-Tuning:
Adapt pre-trained models for specific tasks to achieve optimal performance and relevance. Data Preparation:
Conduct data cleaning and preprocessing to ensure high-quality inputs for modeling. Collaboration:
Work alongside software engineers, product managers, and business analysts to seamlessly integrate AI solutions into existing systems. Documentation:
Produce thorough documentation of methodologies and results and communicate findings to non-technical stakeholders. Mentorship:
Provide guidance and mentorship to junior data scientists, fostering their professional growth. Partnerships:
Collaborate closely with business and technology teams to develop ML models that enhance performance metrics like star ratings and care gap reduction. Effective Communication:
Present complex analytical concepts clearly to diverse audiences and manage the delivery of analytical projects. Industry Insights:
Utilize various tools and techniques to derive insights from current industry trends. Qualifications Required Education:
Master's Degree in Computer Science, Data Science, Statistics, or a related field. Experience:
10+ years as a data scientist, preferably with healthcare experience; candidates from other industries will also be considered. Familiarity with big data technologies, relational databases, and SDLC concepts is essential. Technical Skills:
Proficient in Python and R, with experience in TensorFlow, Keras, or PyTorch. Statistical Analysis:
Strong understanding of statistical methods and ML algorithms, including k-NN, Naive Bayes, and SVM. Agentic Workflows:
Experience designing and implementing workflows that use AI agents for autonomous operations. RAG Knowledge:
Familiar with retrieval-augmented generation techniques. Model Fine-Tuning:
Proven track record of successfully fine-tuning models for specific applications. Data Visualization:
Skilled in tools like Tableau or Power BI. Database Management:
Proficiency in SQL/NoSQL, data warehousing, and ETL processes. Problem-Solving:
Excellent analytical skills and innovative thinking. Preferred Education: PhD or equivalent experience is advantageous. Preferred Experience: Experience with cloud platforms such as Databricks, Snowflake, or Azure AI Studio. Familiarity with NLP and computer vision techniques. EOE Statement:
We are an Equal Opportunity Employer M/F/D/V. Pay Range:
$117,731 - $275,491 annually. Actual compensation will be determined based on location, experience, education, and skill level.