Data Scientist IV

CareSource•Dayton, OH

11d•$113,000 - $197,700•Onsite

About The Position

The Data Scientist IV is responsible for leading the design, development, and operationalization of advanced analytics, machine learning, and artificial intelligence solutions that improve healthcare operations, patient outcomes, and organizational performance. This role provides technical leadership across complex initiatives involving predictive modeling, generative AI, Agentic AI, Natural Language Processing (NLP), and large-scale healthcare data science solutions. They influence enterprise AI strategy, establishes technical standards, and mentors data scientists while remaining a hands-on contributor to the development of innovative AI and machine learning capabilities.

Requirements

Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
Eight (8) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations, required
Three (3) years of technical leadership, solution ownership, or mentoring experience required
One (1) year of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflakes) required
Three (3) years of experience delivering LLM and/or generative AI solutions (e.g., prompt engineering, fine-tuning approaches, and/or RAG) from prototype through production required
Technical leadership skills including mentoring, solution architecture, technical review, and ability to influence across cross-functional teams
Expert knowledge of predictive modeling, machine learning, deep learning, NLP, generative AI, and healthcare analytics methodologies
Expertise in modern AI architectures including Agentic AI, Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), evaluation methodologies, and techniques for building scalable, trustworthy AI solutions
Expert knowledge of transformer architectures, deep learning frameworks, and generative modeling concepts, including quality, latency, scalability, and cost tradeoffs
Familiarity with designing and deploying AI-powered document intelligence, OCR, language extraction, annotation, retrieval, and review workflows utilizing modern NLP, LLM, and Agentic AI technologies
Familiarity with MLOps/LLMOps practices including CI/CD, experiment tracking, model and prompt versioning, automated testing, monitoring, observability, and reproducible pipelines
Understanding of AI governance and responsible AI, including privacy-by-design, HIPAA/PHI handling, model risk management, and safeguards against prompt injection and data leakage
Expert knowledge in SQL and working knowledge of data modeling, data quality, and feature engineering for large healthcare datasets
Knowledge of healthcare operations, payer and provider models, and industry trends
Excellent analytical, problem-solving, and critical-thinking skills, including the ability to translate complex data into actionable insights
Strong project/program leadership skills
Excellent written and verbal communication and presentation skills
Knowledge of health care coding including CPT-4, HCPCS, ICD-9/10, DRG and Revenue Codes
Knowledge of managed care required

Nice To Haves

Master's degree preferred
Equivalent years of relevant work experience may be accepted in lieu of required education

Responsibilities

Lead the operationalization of AI and machine learning solutions from prototype through scalable production deployment.
Establish technical standards, design reviews, best practices, and reusable frameworks for AI, machine learning, and advanced analytics solutions.
Stay current on the latest trends in LLMs, NLP, generative AI, and healthcare informatics, integrating relevant advancements into projects to drive continuous innovation.
Design, develop, and oversee predictive modeling, machine learning, and statistical solutions using healthcare data sources including claims, EHR/EMR, laboratory, utilization management, and other clinical and operational datasets; ensure appropriate evaluation, calibration, monitoring, and interpretability.
Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations.
Design, develop, test, deploy, and govern advanced AI solutions including Generative AI, Agentic AI, NLP, deep learning, and retrieval-augmented generation (RAG) capabilities using established MLOps and LLMOps practices.
Provide technical leadership in transitioning AI and machine learning solutions from prototype to scalable production deployment.
Evaluate emerging AI technologies and recommend enterprise adoption strategies based on business value, risk, and scalability.
Ensure compliance with HIPAA/PHI handling and internal governance standards; collaborate with privacy, security, and compliance partners to mitigate risks such as data leakage, prompt injection, and unsafe outputs.
Collaborate with cross-functional stakeholders (clinical leadership, risk adjustment, care management, operations, IT, and analytics partners) to prioritize work, define KPIs, and communicate results, tradeoffs, and roadmap progress.
Serve as a subject matter expert for AI, machine learning, and advanced analytics initiatives across the enterprise.
Perform any other job related duties as requested.