Note: The job is a remote job and is open to candidates in USA. CareSource is a healthcare organization focused on enhancing healthcare operations and patient care. The Applied AI Scientist III will develop advanced analytics solutions using emerging AI technologies to improve health plan performance and guide other data scientists in delivering complex projects.
Responsibilities
- Evaluate emerging technology in LLMs, NLP, Generative AI, and healthcare informatics, integrating these advancements into the projects to drive continuous innovation
- Utilize NLP algorithms and other deep learning techniques to process and analyze unstructured healthcare data, such as clinical notes, patient feedback, and medical literature, to extract meaningful insights
- Lead development from prototype through production in partnership with engineering/architecture
- Co-own operational readiness (evaluation, monitoring requirements, documentation, safeguards) and support post-release tuning of Generative AI solutions (such as prompt engineering and RAG)
- Lead initiatives specializing in NLP and predictive analytics, ensuring the successful delivery of projects that align with organizational goals
- Work closely with interdisciplinary teams across IT, risk adjustment, program integrity, HEDIS, healthcare operations, finance, and clinical departments to identify and capitalize on opportunities for data-driven innovative solutions using cutting-edge emerging technologies
- Develop and implement predictive models, algorithms, and statistical techniques to extract insights from large and complex healthcare datasets
- Define and execute evaluation strategies for ML and LLM solutions, including quality metrics, bias and safety checks, and performance monitoring after deployment
- Utilize machine learning algorithms to identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care
- Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations
- Collaborate with stakeholders to define key performance indicators (KPIs), develop metrics, and create dashboards and reports that effectively communicate insights and support decision-making
- Provide strategic guidance and recommendations to senior leadership based on data analysis and predictive modeling results
- Ensure compliance with data privacy and security regulations, including HIPAA and PHI handling as applicable and maintain the highest standards of data integrity, model governance, and documentation
- Lead cross-functional projects with occasional light project management across teams to deliver results
- Represent the department in project meetings and other meetings that require subject matter knowledge and input
- Perform any other job related duties as requested
Skills
- Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
- Equivalent years of relevant work experience may be accepted in lieu of required education
- Five (5) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations required
- One (1) year of experience in the healthcare industry required
- One (1) year of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflake) required
- One (1) year of experience in development and deployment of an NLP and Generative AI solution in the healthcare industry required
- Strong expertise in statistical modeling, machine learning techniques, and predictive analytics tools such as Python, or R
- Expert in data manipulation, data visualization, and SQL for data extraction and analysis
- Ability to perform advanced statistical analysis and modeling such as linear and non-linear regression, sampling, and Markov chains
- Master's or PhD degree preferred
- Familiarity with Agentic AI, LLM architecture, prompt engineering, RAG, and evaluation metrics
- Familiarity with MLOps and LLMOps practices such as CI/CD, model registry, experiment tracking, automated evaluation, monitoring, and reproducible pipelines
- Understanding of model risk management, AI governance, and responsible AI
- Expertise in emerging technologies and tools such as large language models, natural language understanding, sentiment analysis, named entity recognition, topic modeling, and text classification
- Expertise in Optical Character Recognition (OCR) technologies, including data extraction from scanned documents, forms, and invoices, and proficiency in OCR tools and libraries
- Familiarity with web app and API development and deployment in production environment
- Detailed knowledge of healthcare data, including medical and pharmacy claims, EMR data, HIE data, UM data, and demographic and population data
- Knowledge of healthcare operations, payer and provider models, and industry trends
- Proficient in feature engineering techniques and exploratory data analysis
- Knowledge of optimization techniques and artificial intelligence methods
- Excellent analytical, problem-solving, and critical-thinking skills, with the ability to translate complex data into actionable insights
- Strong project management skills, with the ability to lead and prioritize multiple projects simultaneously in a fast-paced environment
- Excellent written and verbal communication and presentation skills, with the ability to convey technical concepts to non-technical stakeholders
- Comfortable reading academic research papers and applying them in the models
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