AI​/ML Lead​/Surgical Robotics - OTTAVA

Company Overview

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 Med Tech, 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.

The Staff Engineer in Analytics and AI/ML for Digital Manufacturing is dedicated to advancing data‑driven manufacturing within our supply chain operations. This role involves leading the design, development, and implementation of analytics and artificial intelligence/machine learning solutions that provide both diagnostic and predictive insights to support real‑time performance management and informed decision‑making in intelligent, compliant operations.

• Define technical requirements and architecture for analytics and AI/ML solutions across manufacturing environments (edge, OT, and cloud).
• Lead end‑to‑end delivery of analytics and ML solutions, including data ingestion, feature engineering, model development, validation, deployment, and lifecycle management.
• Design, implement, and operate production‑grade pipelines and inference (batch and real‑time), with monitoring and SLAs for latency, availability, and throughput.
• Translate manufacturing challenges (yield, downtime, quality, throughput) into measurable use cases with clear KPIs and expected ROI.
• Establish MLOps and governance practices (model versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated manufacturing expectations (CSV, GxP where applicable).
• Partner with Manufacturing Engineering, Operations, Quality, IT, and R&D to prioritize and scale high‑value use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and translate them into scalable analytics; ensure adoption through documentation, playbooks, training, and stakeholder engagement.
• Apply statistical methods and experimentation (DOE, SPC, capability analysis) to quantify drivers, validate improvements, and support continuous improvement.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
• 7+ years of experience delivering analytics and/or ML solutions in production environments (manufacturing, supply chain, healthcare/Med Tech, or other regulated industries preferred).
• Proven track record delivering ML/AI solutions into production at scale; experience in manufacturing or industrial/OT environments strongly preferred.
• Experience with manufacturing and industrial data sources (e.g., MES, OPC UA, PLC logs, telemetry, sensors) and translating domain requirements into deployable ML solutions.
• Strong Python skills; experience with ML libraries (scikit‑learn, Tensor Flow, PyTorch) and data processing frameworks (Spark/PySpark).
• Hands‑on MLOps experience, including orchestration, CI/CD, model serving, monitoring/observability, automated retraining, and experiment tracking (MLflow).
• Proficiency in SQL and data modeling; familiarity with lakehouse/data lake patterns (e.g., Delta) and cloud data services (AWS or Azure equivalents), including secure architecture design.
• Applied expertise in time‑series and process analytics (anomaly detection, forecasting, classification/regression), including feature engineering and model interpretability/performance evaluation.
• Experience with model governance, validation, and compliance in regulated environments; familiarity with data governance, security, and role‑based access controls (CSV/GxP where applicable).
• Strong communication and stakeholder‑management skills, including the ability to document architecture/validation artifacts and present to technical and non‑technical audiences.

• Experience with cloud analytics and lakehouse platforms and orchestration (e.g., Databricks, Spark/Delta) and effective collaboration with data engineering teams.
• Familiarity with…