Lead Data Scientist

Role Overview We are seeking an experienced Lead Data Scientist with a strong background in the healthcare domain and hands-on exposure to AI architecture. This role requires a senior individual who has led data science initiatives end-to-end while collaborating closely with engineering, product, and AI architecture teams to design scalable, production-ready AI solutions. The ideal candidate has a balance of strategic leadership, deep technical expertise, and healthcare domain understanding, enabling them to translate complex business and clinical problems into impactful data-driven solutions. Key Responsibilities • Lead the design, development, and deployment of advanced data science and machine learning solutions within the healthcare domain • Act as a technical lead, guiding data scientists and collaborating with AI Architects to ensure scalable, secure, and compliant AI systems • Architect and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and deployment • Work with structured and unstructured healthcare data such as EHR/EMR, claims, clinical notes, imaging metadata, and operational data • Partner with stakeholders to define problem statements, success metrics, and data strategies aligned with clinical and business goals • Ensure models comply with healthcare regulations and standards, including HIPAA and data privacy best practices • Evaluate and recommend tools, frameworks, and cloud-based solutions for ML and AI workloads • Oversee model monitoring, performance tuning, explainability, and governance in production environments • Contribute to AI strategy, solution roadmaps, and architectural decisions alongside AI and platform teams Required Qualifications • 7+ years of experience in data science, with recent experience in a Lead Data Scientist or similar senior role • Strong experience working in the healthcare domain (payer, provider, life sciences, digital health, or health tech) • Hands-on experience designing and deploying ML models in production environment • Solid understanding of AI architecture concepts, including scalable ML systems, MLOps, and cloud-based deployment • Proficiency in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch • Experience with SǪL and big data technologies (e.g., Spark, Databricks, Snowflake) • Strong understanding of data modeling, statistics, and machine learning algorithms • Experience working in cloud environments such as AWS, Azure, or GCP. • Excellent communication skills with the ability to explain complextechnical concepts to non-technical stakeholders. Preferred Qualifications • Prior experience collaborating closely with or acting as an AI Architect on enterprise-scale solution. • Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Azure ML). • Familiarity with NLP, computer vision, or generative AI use cases in healthcare. • Knowledge of healthcare interoperability standards such as HL7, FHIR, or ICD codes. • Experience leading cross-functional teams in a contract or consulting environment.