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# Data Science Tech Lead

- **Company:** CodeBase Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Governance, Monitoring of Systems, Python (Programming Language), Machine Learning, Feature Store, Apache Spark, Deep Learning, Generative AI, Machine Learning Operations, Model Explainability, Databricks
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pou8gromch

## About the Role

Core Skills & Technologies

 * Python
 * Machine Learning
 * Deep Learning
 * AI / GenAI
 * AWS
 * Databricks
 * Spark
 * Survival Analysis
 * Model Explainability
 * MLOps
 * Healthcare Data
 * Solution Design, * 7+ years of data science and machine learning experience, delivering models that reached production or drove real business decisions.
 * 3+ years leading machine learning or data science projects end to end - from problem framing through deployment.
 * 3+ years working directly with business stakeholders and product owners: managing expectations, owning delivery, and driving a high-visibility workstream under tight deadlines.
 * Significant solution-design experience for building machine learning systems, not just individual models.
 * Strong hands-on expertise in Python and the modern ML/DL ecosystem.
 * Deep, practical understanding of machine learning and deep learning - able to choose the right approach and reason about tradeoffs, evaluation, and failure modes.
 * Experience with AWS and Databricks for building and deploying data/ML solutions at scale.
 * Experience working with healthcare data (and awareness of the associated data-quality, privacy, and governance realities).
 * Demonstrated leadership presence: self-motivated, driven to deliver results, and able to earn the confidence of both technical teams and senior stakeholders., * Experience in pharmacy, specialty pharmacy, or clinical/patient-outcomes domains, with working familiarity of the relevant datasets (therapy, dosing, adverse events, discontinuation, claims).
 * Familiarity with time-to-event / survival analysis and its application to intervention-timing problems.
 * Experience deploying models into clinical or operational workflows with human-in-the-loop decisioning and measurable outcome validation (e.g., controlled rollouts).
 * Exposure to Generative AI / agentic approaches and a pragmatic view of where they fit in a regulated setting.
 * Experience with data governance, PHI/HIPAA constraints, and model documentation in a regulated environment.
 * Familiarity with MLOps practices: feature stores, model monitoring, and reproducible pipelines.