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The CenterWell Centralized Data & Analytics team is seeking a highly hands-on Data Scientist 2 to lead the design, development, and scaling of predictive models, LLM- and domain-specific SLM-based solutions, and agentic AI systems for Home Health applications.

This role operates at the intersection of applied AI innovation and real-world production impact, with significant ownership in building production-grade AI systems trained on clinical data. You will combine advanced analytics and machine learning with modern AI techniques, along with strong MLOps, cloud, and Responsible AI practices, to deliver solutions that drive measurable business and patient outcomes.

Key Responsibilities

AI Systems & Intelligent Modeling

• Own the design and development of end-to-end intelligent systems combining predictive modeling, LLMs, and domain-specific SLMs

• Design and deploy predictive models (e.g., classification, regression, ranking, time series forecasting, anomaly detection, churn and risk modeling, and recommender systems), and integrate these signals into LLM- and SLM-based systems (e.g., RAG pipelines, decision engines)

• Build and fine-tune proprietary SLMs trained on clinical notes and healthcare corpora

• Develop and deploy LLM-based and agentic AI systems for reasoning, automation, and decision support

• Engineer and optimize models across structured and unstructured data (tabular, text, and image)

Clinical NLP, Multimodal AI & Innovation

• Develop advanced clinical NLP solutions using embeddings, semantic search, and domain-specific models (e.g., ClinicalBERT, PubMedBERT)

• Extract insights from large-scale unstructured healthcare data, including clinical text corpora

• Build and extend multimodal AI capabilities, including computer vision models (e.g., image classification) as complementary systems

• Prototype and explore emerging approaches in LLMs, SLMs, and agentic AI to drive continuous innovation

• Translate AI outputs into actionable clinical and business insights

Delivery, MLOps & Impact

• Lead end-to-end delivery, from rapid prototyping and MVP development to scalable production systems

• Partner with engineering, product, and business stakeholders to align AI solutions with strategic goals

• Deploy and monitor ML/LLM/SLM systems, including model performance, drift, bias, and impact

• Implement MLOps best practices, including CI/CD, model lifecycle management, and scalable cloud deployment (Azure/AWS)

• Drive Responsible AI practices, including explainability, governance, and compliance in healthcare environments

Growth & Impact

• Opportunity to shape the direction of applied AI systems in a rapidly evolving healthcare domain

• Exposure to cutting-edge techniques across LLMs, SLMs, and agentic AI

• Clear path to expanded ownership, technical leadership, and strategic influence

Why This Role

• Build real-world AI systems deployed at scale, not just prototypes

• Develop domain-specific SLMs on clinical data—a high-impact, differentiated capability

• Work on meaningful problems that directly improve patient outcomes in Home Health

• Operate at the intersection of predictive AI, SLMs, and agentic systems

• Take ownership of solutions that drive measurable business and clinical impact

Use your skills to make an impact
 

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience

• 4+ years of experience in data science, machine learning, or applied AI, with strong foundations in statistical modeling

• Proven experience developing and deploying LLM and/or SLM-based systems in real-world environments

• Hands-on expertise fine-tuning domain-specific models (e.g., LoRA, QLoRA, RLHF, DAPT, DPO)

• Strong experience in predictive modeling, including feature engineering, validation, and production deployment

• Experience working with structured and unstructured data, particularly NLP and text-based systems domain-specific models (e.g., ClinicalBERT, PubMedBERT)

• Proficiency with modern AI/ML ecosystems, including Hugging Face, PyTorch/TensorFlow, Databricks, and MLOps practices

• Ability to independently own and drive complex AI solutions from concept to productio