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.
- Business Collaboration: Partner closely with business and other technology teams to build ML models which helps in improving Star ratings, reduce care gap and other business objectives.
- Communication: Present complex analytical information to all levels of audiences in a clear and concise manner; collaborate with analytics team and manage delivery of analytical projects as appropriate.
- Additional Duties: 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.
- Industry Insights: 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 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
- Technical Proficiency: Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch.
- Statistical Analysis: Excellent understanding of statistical methods and machine learning algorithms, including k-NN, Naive Bayes, SVM, and neural networks.
- Experience with Agentic Workflows: Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations.
- RAG Techniques: Knowledge of retrieval-augmented generation techniques and their application in enhancing AI model outputs.
- Model Fine-Tuning Expertise: Proven experience in fine-tuning models for specific tasks, ensuring they meet the required performance metrics.
- Data Visualization: Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively.
- Database Management: Experience with SQL and NoSQL databases, data warehousing, and ETL processes.
- Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges.
PREFERRED EDUCATION: PHD or additional experience
PREFERRED 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.
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To all current Molina employees: If you are interested in applying for this position, please apply through the intranet job listing.
Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.
Pay Range: $117,731 - $275,491 / ANNUAL
*Actual compensation may vary from posting based on geographic location, work experience, education and/or skill level.
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