AI/ML Lead/Surgical Robotics - OTTAVA
Johnson & Johnson
June 28, 2026
On-site
Santa Clara, CA
IT
Purpose:
The Engineer in Analytics and AI/ML for Digital Manufacturing advances data-driven manufacturing in supply chain operations by leading analytics and AI/ML solutions for diagnostic and predictive insights supporting real-time performance management in compliant operations.
Key Responsibilities:
- Define technical requirements and architecture for analytics and AI/ML across edge, OT, and cloud.
- Lead end-to-end analytics/ML delivery: ingestion, feature engineering, model development/validation, deployment, and lifecycle management.
- Design/operate production-grade batch and real-time pipelines and inference with monitoring and SLAs (latency, availability, throughput).
- Translate manufacturing challenges (yield, downtime, quality, throughput) into use cases with KPIs and expected ROI.
- Establish MLOps/governance (model versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated expectations (CSV/GxP as applicable).
- Partner with Manufacturing Engineering, Operations, Quality, IT, and R&D to prioritize and scale use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and drive adoption (docs, playbooks, training).
- Use statistical methods/experimentation (DOE, SPC, capability analysis) for continuous improvement.
Qualifications:
- BS/MS in Computer Science, Data Science, Statistics, Engineering, or related quantitative field.
- 7+ years delivering analytics and/or ML in production (manufacturing/supply chain/healthcare/MedTech or other regulated industries preferred).
Required Skills:
- Proven ML/AI deployment at scale in manufacturing/industrial/OT environments.
- Experience with MES/OPC UA/PLC logs/telemetry/sensors and translating domain needs into deployable ML.
- Python; ML libraries (scikit-learn, TensorFlow, PyTorch) and Spark/PySpark.
- MLOps: orchestration, CI/CD, model serving, monitoring/observability, automated retraining, experiment tracking (e.g., MLflow).
- SQL/data modeling; lakehouse/data lake patterns (e.g., Delta) and secure cloud architecture (AWS/Azure equivalents).
- Time-series/process analytics (anomaly detection, forecasting, classification/regression) with interpretability/performance evaluation.
- Model governance/validation/compliance in regulated settings (CSV/GxP where applicable); data governance/security/RBAC.
- Strong stakeholder communication and ability to present technical/non-technical artifacts.
Preferred:
- Cloud analytics/lakehouse and orchestration (e.g., Databricks, Spark/Delta) and collaboration with data engineering.
- Digital manufacturing standards familiarity (ISA-95/ISA-88).
- Visualization/decision-support tools (Power BI, Tableau) and dashboarding.
Benefits (pay transparency section):
- Eligible for medical, dental, vision, life insurance, disability, business accident insurance, group legal; retirement plan and 401(k); long-term incentive.
- Time off includes vacation (120 hrs/yr) and sick time (40 hrs/yr); plus other listed leave types.
Base pay range: $118,000.00–$179,035.50.
The Engineer in Analytics and AI/ML for Digital Manufacturing advances data-driven manufacturing in supply chain operations by leading analytics and AI/ML solutions for diagnostic and predictive insights supporting real-time performance management in compliant operations.
Key Responsibilities:
- Define technical requirements and architecture for analytics and AI/ML across edge, OT, and cloud.
- Lead end-to-end analytics/ML delivery: ingestion, feature engineering, model development/validation, deployment, and lifecycle management.
- Design/operate production-grade batch and real-time pipelines and inference with monitoring and SLAs (latency, availability, throughput).
- Translate manufacturing challenges (yield, downtime, quality, throughput) into use cases with KPIs and expected ROI.
- Establish MLOps/governance (model versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated expectations (CSV/GxP as applicable).
- Partner with Manufacturing Engineering, Operations, Quality, IT, and R&D to prioritize and scale use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and drive adoption (docs, playbooks, training).
- Use statistical methods/experimentation (DOE, SPC, capability analysis) for continuous improvement.
Qualifications:
- BS/MS in Computer Science, Data Science, Statistics, Engineering, or related quantitative field.
- 7+ years delivering analytics and/or ML in production (manufacturing/supply chain/healthcare/MedTech or other regulated industries preferred).
Required Skills:
- Proven ML/AI deployment at scale in manufacturing/industrial/OT environments.
- Experience with MES/OPC UA/PLC logs/telemetry/sensors and translating domain needs into deployable ML.
- Python; ML libraries (scikit-learn, TensorFlow, PyTorch) and Spark/PySpark.
- MLOps: orchestration, CI/CD, model serving, monitoring/observability, automated retraining, experiment tracking (e.g., MLflow).
- SQL/data modeling; lakehouse/data lake patterns (e.g., Delta) and secure cloud architecture (AWS/Azure equivalents).
- Time-series/process analytics (anomaly detection, forecasting, classification/regression) with interpretability/performance evaluation.
- Model governance/validation/compliance in regulated settings (CSV/GxP where applicable); data governance/security/RBAC.
- Strong stakeholder communication and ability to present technical/non-technical artifacts.
Preferred:
- Cloud analytics/lakehouse and orchestration (e.g., Databricks, Spark/Delta) and collaboration with data engineering.
- Digital manufacturing standards familiarity (ISA-95/ISA-88).
- Visualization/decision-support tools (Power BI, Tableau) and dashboarding.
Benefits (pay transparency section):
- Eligible for medical, dental, vision, life insurance, disability, business accident insurance, group legal; retirement plan and 401(k); long-term incentive.
- Time off includes vacation (120 hrs/yr) and sick time (40 hrs/yr); plus other listed leave types.
Base pay range: $118,000.00–$179,035.50.