Key Responsibilities:

• The ability to design and develop ML, Gen AI, NLP, LLM Models for AI data pipelines. Model components will include data ingestion, preprocessing, Retrieval Augmented Generation (RAG), NLP/LLM model development, fine-tuning and prompt engineering.

• Analyze large, complex healthcare datasets including electronic health records (EHR) and claims data

• Develop statistical models for patient risk stratification, treatment optimization, population health management, and revenue cycle optimization

• Build models for clinical decision support, patient outcome prediction, care quality improvement, and revenue cycle optimization

 • Create and maintain automated data pipelines for real-time analytics and reporting

• Work with healthcare data standards (HL7 FHIR, ICD-10, CPT, SNOMED CT) and ensure regulatory compliance

• Develop and deploy models in cloud environments while creating visualizations for stakeholders

• Present findings and recommendations to cross-functional teams including clinicians, product managers, and executives

Qualifications required:

• Bachelor's degree in data science, Statistics, Computer Science, Mathematics, or related quantitative field

• 5-7 years of hands-on experience in data science, analytics, or machine learning roles

 • Knowledge of ML Ops practices deploying and operationalize AI models.

• Familiarity with cloud platforms (Azure, AWS, GCP) and containerized deployments (Docker, Kubernetes).

 • Demonstrated experience working with large datasets and statistical modeling

• Proficiency in Python or R for data analysis and machine learning

 • Experience with SQL and database management systems

• Knowledge of machine learning frameworks such as scikit-learn, TensorFlow, PyTorch

• Familiarity with data visualization tools such as Tableau, Power BI, matplotlib, ggplot2

 • Experience with version control systems (Git) and collaborative development practices

• Strong foundation in statistics, hypothesis testing, and experimental design

• Experience with supervised and unsupervised learning techniques

• Knowledge of data preprocessing, feature engineering, and model validation

• Understanding of A/B testing and causal inference methods

• Experience with cloud platforms and big data technologies such as Spark, Hadoop

What You’ll Need to Be Successful (Required Skills):

• Large Language Model (LLM) Experience: At least 5 years of hands-on experience working with pretrained language models (GPT, BERT, T5) including fine-tuning, prompt engineering, and model evaluation techniques. LLM experience should include Claude, Llama models. Understanding of RAG techniques is a critical skill.

• Generative AI Frameworks: Proficiency with generative AI libraries and frameworks such as Hugging Face Transformers, Lang Chain, OpenAI API, or similar platforms for building and deploying AI applications

• Prompt Engineering and Optimization: Experience designing, testing, and optimizing prompts for various use cases including text generation, summarization, classification, and conversational AI applications

• Vector Databases and Embeddings: Knowledge of vector similarity search, embedding models, and vector databases (Pinecone, we aviate, Chroma) for building retrieval-augmented generation (RAG) systems

• AI Model Evaluation: Experience with evaluation methodologies for generative models including BLEU scores, ROUGE metrics, human evaluation frameworks, and bias detection techniques

• Multi-modal AI Systems: Familiarity with multi-modal generative models combining text, images, and other data types, including experience with vision-language models and cross-modal applications

• AI Safety and Alignment: Understanding of responsible AI practices including content filtering, bias mitigation, hallucination detection, and techniques for ensuring AI outputs align with business requirements and ethical guidelines

Preferred Skills:

• At least 1 year of experience working with healthcare data or in healthcare IT environments

• Familiarity with electronic health record (EHR) systems and healthcare workflows

• Understanding of healthcare data privacy regulations (HIPAA, HITECH)

• Knowledge of clinical data standards and interoperability frameworks

• Knowledge of MLOps practices and model deployment pipelines

• Familiarity with natural language processing for clinical text analysis

• Experience with time series analysis for patient monitoring data

Netsmart is proud to be an equal opportunity workplace and is an affirmative action employer, providing equal employment and advancement opportunities to all individuals. We celebrate diversity and are committed to creating an inclusive environment for all associates. All employment decisions at Netsmart, including but not limited to recruiting, hiring, promotion and transfer, are based on performance, qualifications, abilities, education and experience. Netsmart does not discriminate in employment opportunities or practices based on race, color, religion, sex (including pregnancy), sexual orientation, gender identity or expression, national origin, age, physical or mental disability, past or present military service, or any other status protected by the laws or regulations in the locations where we operate.

Netsmart desires to provide a healthy and safe workplace and, as a government contractor, Netsmart is committed to maintaining a drug-free workplace in accordance with applicable federal law. Pursuant to Netsmart policy, all post-offer candidates are required to successfully complete a pre-employment background check, which is provided at Netsmart’s sole expense.