Job Description
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.
• 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.
• AI Model Deployment and Monitoring: Deploy AI models into production environments, monitor their performance, and adjust as necessary to maintain accuracy and effectiveness.
• Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes.
• 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.
• Documentation and Reporting: Create comprehensive documentation of models, methodologies, and results; communicate findings clearly to non-technical stakeholders.

Job Qualifications

REQUIRED EDUCATION:

Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field

REQUIRED EXPERIENCE/KNOWLEDGE, SKILLS & ABILITIES:

• 3+ 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
• 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:

Master’s degree in computer science, Data Science, Statistics, or a related field

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.
  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: $88,453 - $206,981 / ANNUAL

• Actual compensation may vary from posting based on geographic location, work experience, education and/or skill level.

• 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.
• AI Model Deployment and Monitoring: Deploy AI models into production environments, monitor their performance, and adjust as necessary to maintain accuracy and effectiveness.
• Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes.
• 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.
• Documentation and Reporting: Create comprehensive documentation of models, methodologies, and results; communicate findings clearly to non-technical stakeholders