AI/ML Lead/Surgical Robotics - OTTAVA
Johnson & Johnson
August 10, 2026
On-site
Santa Clara, CA
IT
Purpose:
Engineer in Analytics and AI/ML for Digital Manufacturing to advance data-driven manufacturing in supply chain operations by leading analytics/AI-ML for diagnostic and predictive insights for real-time performance management.
Key Responsibilities:
- Define technical requirements/architecture for analytics + AI/ML across edge, OT, and cloud.
- Lead end-to-end ML delivery: data ingestion, feature engineering, model dev/validation, deployment, and lifecycle management.
- Build production-grade batch/real-time inference pipelines with monitoring and SLAs (latency, availability, throughput).
- Convert manufacturing challenges (yield, downtime, quality, throughput) into KPI-driven use cases with expected ROI.
- Establish MLOps/governance (versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated expectations.
- Partner cross-functionally to prioritize/scale use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and drive adoption via documentation/training.
- Use statistical methods/experimentation (DOE, SPC, capability analysis) to quantify drivers and validate improvements.
Qualifications:
- BS/MS in CS, Data Science, Statistics, Engineering, or related quantitative field.
- 7+ years delivering analytics and/or ML in production (manufacturing/supply chain/regulated preferred).
Required Skills:
- ML/AI production delivery at scale (manufacturing/industrial/OT preferred).
- Manufacturing/industrial data sources (MES, OPC UA, PLC logs, telemetry, sensors).
- 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 AWS/Azure secure architecture.
- Time-series/process analytics; feature engineering; interpretability/performance evaluation.
- Model governance/validation/compliance in regulated environments.
- Communication with technical/non-technical audiences.
Preferred:
- Cloud analytics/lakehouse/orchestration (e.g., Databricks, Spark/Delta) and collaboration with data engineering.
- Digital manufacturing standards (ISA-95/ISA-88).
- Power BI/Tableau dashboards.
Benefits (as stated):
- Medical, dental, vision, life insurance; short-/long-term disability; retirement plan/pension and 401(k); long-term incentive; vacation (120 hrs/yr), sick time (40 hrs/yr; 56 hrs in WA), holiday pay (13 days); parental leave (480 hrs/yr); and other listed time-off programs.
Engineer in Analytics and AI/ML for Digital Manufacturing to advance data-driven manufacturing in supply chain operations by leading analytics/AI-ML for diagnostic and predictive insights for real-time performance management.
Key Responsibilities:
- Define technical requirements/architecture for analytics + AI/ML across edge, OT, and cloud.
- Lead end-to-end ML delivery: data ingestion, feature engineering, model dev/validation, deployment, and lifecycle management.
- Build production-grade batch/real-time inference pipelines with monitoring and SLAs (latency, availability, throughput).
- Convert manufacturing challenges (yield, downtime, quality, throughput) into KPI-driven use cases with expected ROI.
- Establish MLOps/governance (versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated expectations.
- Partner cross-functionally to prioritize/scale use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and drive adoption via documentation/training.
- Use statistical methods/experimentation (DOE, SPC, capability analysis) to quantify drivers and validate improvements.
Qualifications:
- BS/MS in CS, Data Science, Statistics, Engineering, or related quantitative field.
- 7+ years delivering analytics and/or ML in production (manufacturing/supply chain/regulated preferred).
Required Skills:
- ML/AI production delivery at scale (manufacturing/industrial/OT preferred).
- Manufacturing/industrial data sources (MES, OPC UA, PLC logs, telemetry, sensors).
- 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 AWS/Azure secure architecture.
- Time-series/process analytics; feature engineering; interpretability/performance evaluation.
- Model governance/validation/compliance in regulated environments.
- Communication with technical/non-technical audiences.
Preferred:
- Cloud analytics/lakehouse/orchestration (e.g., Databricks, Spark/Delta) and collaboration with data engineering.
- Digital manufacturing standards (ISA-95/ISA-88).
- Power BI/Tableau dashboards.
Benefits (as stated):
- Medical, dental, vision, life insurance; short-/long-term disability; retirement plan/pension and 401(k); long-term incentive; vacation (120 hrs/yr), sick time (40 hrs/yr; 56 hrs in WA), holiday pay (13 days); parental leave (480 hrs/yr); and other listed time-off programs.