Lead Data Scientist

Requirements:

• 7+ years of experience building and deploying production AI/ML systems.
• Strong emphasis on solving the underlying data problem, rather than focusing primarily on model selection or infrastructure.
• Deep experience working with complex, domain-specific data at scale, including noisy real-world datasets and historical decision records.
• Proven experience designing training data, labeling strategies, data curation, and evaluation datasets where data quality was a core ML challenge.
• Experience handling high-cardinality label and feature spaces and extracting meaningful patterns from large datasets.
• Strong expertise across multiple AI/ML areas, including NLP, traditional machine learning, and generative AI/LLMs.
• Proven experience taking AI/ML systems from experimentation to production.
• Expert-level proficiency in PyTorch or TensorFlow, with experience in model training and experimentation at scale.
• Experience designing cloud-native AI architectures on AWS, Azure, or GCP.
• Hands-on experience with Docker and Kubernetes for production AI/ML workloads.
• Strong understanding of ML system architecture, data pipelines, model training, inference, and evaluation.

Preferred:

• Experience building self-improving AI systems, including active learning pipelines, feedback-loop architectures, outcome-based learning, and RLHF or reward-based learning systems.
• Experience with large-scale data mining across high-cardinality feature, taxonomy, or code spaces.
• Experience identifying rare but high-value patterns in large datasets.
• Experience building AI systems that learn from historical decisions and labeled outcomes, such as autonomous decision engines, recommendation systems, or adaptive classification systems.
• Experience with MLOps, including ML CI/CD, model versioning, experiment tracking, model monitoring, and deployment governance.
• Experience in healthcare technology, medical AI, or other regulated industries.
• Familiarity with HIPAA-compliant system design, audit trails, data governance, and compliance requirements.
• Exposure to healthcare coding systems such as ICD-10, CPT, or SNOMED-CT is a plus.