Principal- AI and Data Sciences

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: Scientific/Technology All Job Posting Locations: Irvine, California, United States of America Job Description: 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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