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Role overview

Johnson and Johnson MedTech sector is currently recruiting for a Principal AI, Data Science & Databricks with 2–3 years of hands-on experience building and validating machine learning prediction models for MedTech. The position will be in Irvine, CA and Raritan NJ. Additional travel up to 25% may be required.

The ideal candidate will be proficient in Databricks and Python, experienced with both structured-data and unstructured-data AI (e.g., tabular models plus NLP / image models), and able to create, evaluate, and perform regression testing of prediction models under regulated product-development constraints.

Key responsibilities

• Design, build, and maintain end-to-end ML solutions on Databricks for prediction problems using structured and unstructured data.
• Implement robust data pipelines (ETL/ELT) and feature engineering using Spark / PySpark and Delta Lake.
• Develop, train, validate, and optimize supervised and unsupervised models (regression, classification, time-series, NLP, computer vision) using Python ML frameworks (Prophet, XGBoost/LightGBM, Hugging Face).
• Define and implement model evaluation strategies and metrics appropriate for commercial use
• Establish and run regression test suites for prediction models to detect performance drift across data, code, and infrastructure changes.
• Apply explainability/interpretability techniques and produce model risk and performance reports for stakeholders and auditors.
• Package, version, and register models (MLflow or equivalent) and support deployment and monitoring (CI/CD, A/B testing, model monitoring, alerting).
• Troubleshoot production issues, investigate model failures, and implement fixes with appropriate validation and traceability.
• Understand and enhance the Structured data AI and Unstructured data AI models
• Integrate the Structured and Structured data models using Agentic framework and API’s
• Working knowledge REACT, JavaScript and SQL Server

Required qualifications

• 2–3 years of professional experience in an AI/ML or data science engineering role in the MedTech industry (or closely related regulated healthcare environment).
• Strong hands-on experience with Databricks (workspace use, notebooks, jobs, clusters, Delta Lake, MLflow integration).
• Proficient in Python and common ML/data libraries (Prophet, PySpark, XGBoost/LightGBM, Hugging Face).
• Demonstrated experience building and evaluating prediction models for structured data (tabular) and unstructured data (text, images, signals).
• Experience creating and maintaining regression tests for models and pipelines; knowledge of unit and integration testing for ML components.
• Solid understanding of ML model evaluation, validation, overfitting mitigation, cross-validation, and hyperparameter tuning.
• Experience with data engineering concepts: ETL/ELT, data partitioning, feature stores, SQL, and PySpark performance tuning.
• Familiarity with model lifecycle tooling: MLflow, version control (git), CI/CD pipelines, containerization (Docker), and cloud services (Azure, AWS, or GCP).
• Working knowledge of MedTech regulatory considerations (e.g., documentation for verification/validation, traceability, data privacy regulations such as HIPAA), and secure handling of clinical data.

Preferred qualifications

• BS/MS in Computer Science, Data Science, Statistics, Biomedical Engineering, or related field.
• Experience with time-series forecasting, REACT programming, Python
• Experience deploying models in commercial or medical device environments and running post-deployment monitoring for data drift, concept drift, and performance degradation.
• Experience with natural language processing (images, text, notes).

Technical stack (typical)

• Databricks (notebooks, jobs, Delta Lake, MLflow)
• Python, PySpark, pandas, NumPy
• Prophet, XGBoost, LightGBM
• SQL, PySpark performance tuning
• Cloud: Azure preferred
• REACT, JavaScript and SQL Server
• CI/CD tooling (Azure DevOps / GitHub Actions / Jenkins)

Human skills

• Strong problem-solving and debugging skills with attention to reproducibility and traceability.
• Clear communicator able to translate technical results for Commercial and Technology stakeholders.
• Collaborative team player, comfortable working across cross-functional teams (commercial, technology, business).
• High standards for data quality, documentation, and reproducible research practices.

Deliverables and success measures (examples)

• Production-ready Databricks pipelines that reliably prepare and serve feature data with automated tests.
• Predictive models with validated performance against pre-defined clinical acceptance criteria and documented validation artifacts.
• Automated regression tests that prevent unintended model degradations and reduce time-to-detect issues.
• Clear model performance and risk reports, and deployment of monitoring dashboards that detect drift and trigger remediation.

• Technical phone screen (Python, Databricks, ML fundamentals)

Preferred:

• Understanding of MedTech regulatory processes and GxP considerations is a plus.
• Builds a culture focused on customer outcomes and helping people and organizations succeed.
• Apply customer-centric discovery methods and build compassion with users.
• Advocates business agility and a fail-fast approach focused on measurable outcomes.
• Experience working with integrations, ERPs, and middleware technologies.
• Strong analytical and problem-solving skills; makes informed decisions under uncertainty.

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