Senior Data Scientist (AI & NLP)

About Amplify Health

Position

AI strategy and production systems

• Translate business and healthcare problems into measured AI solutions; define enterprise strategy and architecture for NLP, generative AI, RAG, multimodal AI, and agentic systems.
• Make technical trade-offs and conduct due diligence for models, vector stores, orchestration frameworks, vendors, and build-versus-buy decisions.
• Design production AI systems using LLMs, RAG, NLP, and agents as runtime components; set patterns for planning, tools, permissions, memory, observability, fallbacks, and human oversight.
• Shape reusable AI platforms, knowledge services, evaluation harnesses, guardrails, and LLMOps capabilities.
• Keep productivity assistants and development automation distinct from runtime AI components, and govern and validate each appropriately.

Evaluation, governance, and influence

• Establish evaluation for quality, grounding, retrieval, hallucinations, safety, robustness, bias, privacy, latency, cost, and human oversight.
• Define governance for system design, validation, monitoring, model risk, accountability, auditability, healthcare privacy, security, compliance, fairness, and operational safety.
• Champion AI-assisted development tools with approved usage patterns, quality controls, adoption measures, and safeguards for security, intellectual property, and decision accountability.
• Influence executive decisions and build technical capability through mentorship, standards, training, communities of practice, and thought leadership.

Required qualifications

• 8–10+ years implementing end-to-end AI/ML solutions; 5+ years hands-on NLP, deep learning, and transformer experience; and 3+ years building generative AI solutions including LLM workflows, fine-tuning, evaluation, and RAG.
• Mandatory proficiency in SQL, Python, PySpark, PyTorch, LangGraph, Databricks, MLflow, Azure AI services, and LLM APIs.
• Production-scale AI/ML deployment and monitoring experience; LangChain and LangGraph experience for agentic AI and RAG applications.
• Expertise in GenAI observability, model tracing, LLM evaluation, hallucination detection, performance monitoring, and system optimization.
• Knowledge of responsible AI, including fairness, bias, explainability, safety, privacy, and compliance; experience defining governance and adoption practices.
• Business acumen, executive and cross-functional influence, technical talent development, excellent written and verbal communication, and experience in matrix organisations.

Preferred qualifications

• A bachelor’s or master’s degree in computer science or an equivalent technical field is preferred.
• Healthcare domain experience, healthcare data standards and privacy, regulated environments, and responsible AI in healthcare are preferred or desirable.
• Azure, Databricks or equivalent platforms, MLOps, CI/CD for ML, model monitoring and governance, and scalable deployment experience are preferred.
• Multimodal AI, knowledge graphs, medical text analytics, and clinical decision support exposure are advantageous.

What success looks like