Lead Data Scientist

Requirements:
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.

Skills
aws, azure, data, gcp, pytorch, scientist, tensorflow

Experience
6-9 yrs