Principal Data Scientist - Generative AI, Machine Learning, Python, R - Remote

$141,277 - $275,491/Yr

Molina Healthcare

Full-time - Senior

Remote

Insurance Carriers and Related Activities

About the position

The position is responsible for overseeing data science projects, managing and mentoring a team, and aligning data initiatives with business goals. The role involves leading the development and implementation of data models, collaborating with cross-functional teams, and ensuring ethical data use while communicating complex technical concepts to non-technical stakeholders. The candidate will also lead initiatives on model governance and model ops, focusing on generative AI healthcare solutions to enhance healthcare operations and 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.
• Mentor, coach, and provide guidance to newer data scientists.
• Partner closely with business and other technology teams to build ML models which help in improving Star ratings, reduce care gap and other business objectives.
• Present complex analytical information to all levels of audiences in a clear and concise manner.
• Collaborate with the analytics team, assigning and managing delivery of analytical projects as appropriate.
• Perform other duties as business requirements change, looking out for data solutions and technology-enabled solution opportunities and make referrals to the appropriate team members in building out payment integrity solutions.
• Use a broad range of tools and techniques to extract insights from current industry or sector trends.

Requirements

• Master's Degree in Computer Science, Data Science, Statistics, or a related field.
• 10+ years' work experience as a data scientist preferably in a healthcare environment but candidates with suitable experience in other industries will be considered.
• Knowledge of big data technologies (e.g., Hadoop, Spark).
• Familiar with relational database concepts, and SDLC concepts.
• Demonstrate critical thinking and the ability to bring order to unstructured problems.
• Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch.
• Excellent understanding of statistical methods and machine learning algorithms, including k-NN, Naive Bayes, SVM, and neural networks.
• Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations.
• Knowledge of retrieval-augmented generation techniques and their application in enhancing AI model outputs.
• Proven experience in fine-tuning models for specific tasks, ensuring they meet the required performance metrics.
• Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively.
• Experience with SQL and NoSQL databases, data warehousing, and ETL processes.
• Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges.

Nice-to-haves

• PHD or additional experience.
• Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio etc.) for working with AI workflows and deploying models.
• Familiarity with natural language processing (NLP) and computer vision techniques.

Benefits

• Competitive benefits and compensation package.