Overview

Seeking a highly experienced Lead Applied Data Scientist to lead the discovery, design, and adoption of AI-powered solutions across multiple domains.

Responsibilities

• AI Strategy & Technical Leadership • Drive AI solution strategy for complex and high-impact business problems
• Lead technical design, solution architecture, and AI technology selection decisions
• Establish reusable AI patterns, frameworks, standards, and best practices
• Provide technical leadership and mentorship to Applied Data Scientists and cross-functional teams
• Evaluate emerging AI technologies and recommend enterprise adoption approaches
• Applied AI Solution Development • Translate business challenges into scalable AI, ML, GenAI, and Agentic AI solutions
• Design and develop POCs, prototypes, and reference implementations
• Extensive experience in areas by providing solutions using machine learning, deep learning, NLP, recommendation systems, forecasting, multimodal AI, and Generative AI techniques
• Build reusable assets including prompts, workflows, evaluation frameworks, and implementation accelerators
• Define production-ready solution blueprints to support engineering adoption
• Generative AI & Agentic AI • Design and implement solutions using LLMs, RAG, semantic search, vector retrieval, and prompt engineering techniques
• Develop agentic workflows leveraging orchestration frameworks, tool integration, planning, reasoning, and multi-agent collaboration
• Establish evaluation, guardrail, and governance frameworks for GenAI applications
• Optimize solution performance, quality, and cost efficiency
• Production Readiness & MLOps • Drive successful transition of validated AI solutions into production environments
• Apply AI Development Lifecycle (AIDLC) and MLOps best practices across experimentation and deployment
• Ensure scalability, observability, monitoring, model governance, and operational readiness
• Collaborate with engineering and platform teams to operationalize AI solutions
• Team & Organizational Impact • Lead a small team of Applied Data Scientists while remaining hands-on
• Mentor team members on AI methodologies, experimentation practices, and technical excellence
• Promote knowledge sharing, innovation, and adoption of reusable AI capabilities
• Influence enterprise AI strategy, architecture standards, and capability development

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's preferred
• 15+ years of experience delivering enterprise AI/ML solutions
• Hands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, embeddings, vector databases, agentic systems
• Proven experience leading complex AI initiatives from ideation through production deployment
• Expertise in Python, SQL, PyTorch, and/or TensorFlow
• Solid expertise in machine learning, deep learning, statistical modeling, predictive analytics, and experimentation
• Solid knowledge of AI architecture, MLOps, model governance, and production AI systems
• Demonstrated solid communication, stakeholder management, and technical leadership skills
• Healthcare experience involving Claims, EHR/FHIR, Clinical Analytics, Care Management, or Population Health
• Contributions to patents, publications, AI accelerators, or innovation programs
• AI/ML & Analytics: Machine Learning, Deep Learning, Statistical Modeling, Predictive Analytics
• Applied AI Modeling: Natural Language Processing (NLP), Computer Vision, Time Series Forecasting, Recommendation Systems, Anomaly Detection
• Generative & Agentic AI: Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Agentic AI, Multi-Agent Systems, Semantic Search
• AI Frameworks: PyTorch, TensorFlow, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, LangGraph
• Programming & Data Engineering: Python, SQL, Feature Engineering, Data Pipelines, Model Evaluation, Experimentation Frameworks
• MLOps & Model Lifecycle: MLOps, MLflow, Kubeflow, CI/CD, Model Monitoring, Observability, Drift Detection, Model Registry, Deployment Automation
• AI Deployment Platforms: Azure Machine Learning, AWS SageMaker, Google Vertex AI, Docker, Kubernetes, API-Based Model Serving
• Cloud & Infrastructure: Azure, AWS, GCP, Cloud-Native AI, Hybrid Cloud, On-Premises Deployments
• Vector & Knowledge Systems: Pinecone, FAISS, Weaviate, Chroma, pgvector, Azure AI Search, Neo4j
• Data Platforms: Databricks, Snowflake, BigQuery, Kafka, Lakehouse Architectures
• AI Architecture & Engineering: Enterprise AI Architecture, Scalable ML Systems, AI Solution Design, Feature Stores, AI Platforms, Production AI Deployment
• Responsible AI & Governance: Responsible AI, Explainability, Fairness, AI Governance, Risk Management, SOC 2, HITRUST, HIPAA
• Healthcare Analytics: Healthcare Analytics, Claims, Clinical Data, EHR/HL7/FHIR, ICD/CPT, Risk Adjustment, Population Health, Care Management
• Leadership & Strategy: Technical Leadership, AI Strategy, Innovation, Stakeholder Management, Mentoring, Cross-Functional Collaboration

Nice to have

• PhD or advanced degree in AI, ML, or related field
• Healthcare experience

Education

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's preferred