Lead Data Scientist

Requirements:
8+ years building and deploying production AI/ML systems with demonstrated emphasis on solving the underlying data problem, not just model selection or infrastructure.
Deep experience working with complex, domain-specific data at scale: extracting signal from noisy real-world datasets, building training data from historical decision records, and handling high-cardinality label spaces.
Deep expertise across multiple AI domains (NLP, traditional ML, and Gen AI) with production deployments in each.
Expert-level proficiency in PyTorch or TensorFlow; experience with model training at scale.
Proven track record on AI/ML projects where data quality, labeling strategy, and training data curation were the primary challenges, not just model architecture or deployment.
Experience with cloud-native AI architectures on AWS, Azure, or GCP; containerized deployments (Docker, Kubernetes) for AI workloads.
Preferred:
Experience building self-improving AI systems: active learning pipelines, feedback loop architectures that connect outcome signals back to model updates, or reinforcement learning from human feedback (RLHF) / outcome-based reward signals.
Data mining experience at scale across high-cardinality feature spaces, finding rare but high-value patterns across large taxonomy or code spaces.
Hands-on experience with domain-specific AI systems that learn from historical decisions: e. g., autonomous decision engines, recommendation systems, or classification pipelines that adapt from labeled outcomes over time.
Experience with MLOps tooling: CI/CD for ML, model versioning, monitoring, and deployment governance.
Experience in healthcare technology, medical AI, or regulated industries, including HIPAA-compliant system design and audit trail requirements.
Familiarity with healthcare data standards (HL7 FHIR) or medical coding systems (ICD, CPT).
Experience with continual learning or domain adaptation for specialized vertical AI applications.
Knowledge of FDA guidelines for AI/ML as a medical device (SaMD) or equivalent regulated AI frameworks.
Experience building interpretable AI systems with explainability requirements for clinical or compliance stakeholders.
Experience with ensemble models, meta-learning, or multi-modal architectures combining structured and unstructured data.

Experience
8-12 yrs