Principal AI and Data Sciences

What you'll do

• Design, build, and maintain end-to-end machine learning solutions on Databricks using structured and unstructured data.
• Implement robust data pipelines and feature engineering using Spark, PySpark, and Delta Lake.
• Develop, train, and optimize supervised and unsupervised models using Python frameworks like XGBoost, Prophet, and Hugging Face.
• Establish model evaluation strategies and perform regression testing to detect performance drift across infrastructure changes.
• Apply explainability techniques and produce model risk and performance reports for stakeholders and auditors.
• Package, version, and register models using MLflow while supporting deployment through CI/CD pipelines and monitoring.
• Troubleshoot production issues and investigate model failures to implement validated fixes with full traceability.
• Integrate structured and unstructured data models using Agentic frameworks and APIs.

What we're looking for

• 2–3 years of professional experience in an AI/ML or data science engineering role within the MedTech or regulated healthcare industry.
• Proficiency in Databricks, including workspace use, notebooks, jobs, clusters, Delta Lake, and MLflow integration.
• Proficiency in Python and common machine learning libraries such as Prophet, PySpark, XGBoost, LightGBM, and Hugging Face.
• Experience building and evaluating prediction models for both structured (tabular) and unstructured data (text, images, signals).
• Experience creating regression tests for models/pipelines and knowledge of unit and integration testing for ML components.
• Proficiency in data engineering concepts including ETL/ELT, feature stores, SQL, and PySpark performance tuning.
• Familiarity with model lifecycle tooling such as Git, CI/CD pipelines, Docker, and cloud services like Azure, AWS, or GCP.
• Knowledge of MedTech regulatory considerations, including HIPAA compliance and documentation for verification and validation.
• BS/MS in Computer Science, Data Science, Statistics, Biomedical Engineering, or a related field (preferred).
• Experience with time-series forecasting, REACT programming, and deploying models in medical device environments (preferred).