Senior Data Scientist

Molina Healthcare•Long Beach, CA

About The Position

Provides senior level support for data science activities. Performs data and error analysis to improve models, and cleans and validates data for uniformity and accuracy. Executes data science and statistical analytical experiments methodically to solve various problems and positively impact various health care domains. Develops and deploys advanced machine learning models and artificial intelligence (AI) solutions that enhance Molina products and services. Leverages expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions. Pioneers generative AI health care solutions aimed at revolutionizing health care operations, and enhancing the member experience.

Requirements

At least 6 years of experience as a data scientist, preferably in a health care environment, or equivalent combination of relevant education and experience.
Bachelor's degree in IT, computer science, statistics, business, health care, engineering, etc.
Knowledge of big data technologies (e.g., Hadoop, Spark).
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 (RAG) 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 extract, transform, and load (ETL) processes.
Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges.
Critical-thinking and problem-solving skills.
Ability to meet delivery deadlines.
Strong time-management and organizational skills.
Ability to collaborate cross-functionally in a highly matrixed organization.
Effective verbal and written communication skills.
Experience with Microsoft Project/Jira, Microsoft O365 Cloud (Exchange, Skype, SharePoint, OneDrive, OneNote), Microsoft Office/Office ProPlus (Excel, Word, PowerPoint, Outlook, Teams), Power BI, and applicable software programs proficiency.

Nice To Haves

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.

Responsibilities

Extracts meaningful insights from complex datasets, identifies patterns, and interprets data to inform strategic decision-making.
Designs, develops, and trains machine learning models using a variety of algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Develops and implements agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities.
Employs retrieval-augmented generation (RAG) patterns to improve the performance of language models - ensuring they can access and utilize external knowledge effectively to enhance their outputs.
Fine-tunes pre-trained models for adaptation to specific tasks or datasets - ensuring optimal performance and relevance in various applications.
Prepares data for analysis by performing data cleaning, handling missing values, and removing outliers to ensure high-quality inputs for modeling.
Deploys AI models into production environments, monitors performance, and adjusts as necessary to maintain accuracy and effectiveness.
Collaborates closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes.
Stays current with the latest advancements in AI and machine learning and apply insights to improve existing models and develop new methodologies.
Creates comprehensive documentation of models, methodologies, and results; communicates findings clearly to non-technical stakeholders.
Partners closely with business and other technology teams to build machine learning (ML) models to improve Star ratings, reduce care gaps and support other business objectives.
Presents complex analytical information to all level of audiences in a clear and concise manner; collaborates with analytics team and assigns and manages delivery of analytical projects as appropriate.
Identifies data solutions and technology enabled solution opportunities, and facilitates referrals to appropriate team members in building out payment integrity solutions.
Leverages a broad range of tools and techniques to extract insights from current industry or sector trends.
Provides training and support to new and existing data scientist team members.