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# Principal Data Scientist - Surgical AI

- **Company:** KaashTech LLC
- **Location:** Santa Clara, CA, United States
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Audit Trail, Machine Learning, Azure Machine Learning, Software Deployment, Data Processing, Feature Engineering, Model Validation, Meditech, Software Version Control, Data Pipelines, Databricks
- **Published:** October 16, 2026
- **Apply:** https://www.dice.com/job-detail/1b068bc6-c71d-4a59-a895-a77ba62d3f7d

## About the Role

* 15-20 years of professional experience in data science, machine learning, or related fields
 * Demonstrated expertise in model design, experimentation, and evaluation through production deployment cycles
 * Hands-on experience with surgical AI, multimodal data (surgical video, EHR data, device telemetry), or similar healthcare applications
 * Proven ability to work within PHI and HIPAA compliance frameworks, including de-identified data handling
 * Proficiency with enterprise ML platforms (AWS SageMaker, Databricks, or equivalent)
 * Strong facilitation skills to conduct workshops with clinical stakeholders and translate clinical needs into technical scope
 * Ability and willingness to work onsite at hospital locations throughout the United States
 * Experience with model validation, regulatory documentation, and clinical-grade quality standards, * Background in clinical medicine, surgery, or healthcare operations
 * Experience with video analysis, computer vision, or temporal data modeling
 * Familiarity with FDA regulatory pathways or medical device validation processes
 * Prior work in regulated industries (MedTech, pharmaceuticals, etc.)
 * Experience with model governance, audit trails, and compliance documentation
 * Knowledge of surgical workflow optimization or OR analytics

## Description

* Lead model design and experimentation strategy for surgical AI systems, defining architecture decisions, feature engineering approaches, and validation methodologies based on clinical context and site-specific constraints.
 * Conduct on-site hospital workshops with clinical stakeholders to establish project scope, identify technical constraints, define success metrics, and translate clinical requirements into data science objectives.
 * Partner directly with clinicians throughout the model development lifecycle, ensuring surgical expertise informs every decision from data collection through final evaluation and deployment.
 * Own end-to-end model development and validation, including data curation, feature engineering, model training, performance evaluation, and comprehensive documentation with full lineage tracking and model cards.
 * Design for real-world variability across hospital sites, accounting for differences in data quality, procedure coverage, annotation consistency, surgical equipment (robotic-assisted vs. laparoscopic), and clinical workflows.
 * Implement MedTech-grade validation protocols that maintain complete traceability of datasets, annotations, training runs, evaluation results, and deployed artifacts; serve as one of three required sign-offs before clinical deployment.
 * Collaborate with cross-functional pod teams-Data Engineers and ML Engineers-to build robust data pipelines, feature tables, training infrastructure, model versioning systems, and experiment tracking frameworks.
 * Transfer knowledge and IP fully to client organizations, ensuring hospitals own all models and intellectual property while building internal capability for ongoing model management and improvement.