Sr. Data Scientist

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 https://www.jnj.com

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

New Brunswick, New Jersey, United States of America

Job Description:

Johnson & Johnson is recruiting for a Sr. Data Scientist, located in New Brunswick, New Jersey or Zug, Switzerland.

We’re seeking a Sr Data Scientist to join our Engineering & Property Services digital team. You’ll partner with facilities management, engineering, operations, property management and partner teams to turn building data into actionable insights that optimize asset performance, reduce energy use, improve project portfolio deliverables, mitigate risk and elevate tenant experiences across our property and project portfolios, among other things.

Key Responsibilities:

• Build, validate, and deploy predictive models for predictive maintenance, reliability improvements, and energy optimization (e.g., equipment failure risk, fault detection, sensor anomaly detection).

• Design and run experiments (A/B tests, quasi-experiments) to quantify the impact of operational changes (lighting upgrades, control strategies, sensor deployments) on energy and user experience.

• Integrate data from BMS/EMS, CMMS/EAM, IoT sensors, GIS/CAD, and ERP systems; ensure data quality, lineage, and governance.

• Develop dashboards and reports that translate complex technical findings into actionable guidance for engineers, facilities managers, property leaders, and non-technical partners.

• Collaborate with multi-functional teams to define metrics, supervise portfolio performance, and support capital planning and procurement decisions.

• Document methodologies, modeling assumptions, and versioned artifacts; ensure reproducible workflows and transferability across properties.

• Know the latest smart-building trends, sustainability standards (e.g., energy efficiency initiatives), and regulatory/compliance considerations relevant to facilities management.

• Mentor junior analysts and contribute to the development of standard processes in analytics for property services.

Qualifications

Education:

• Bachelor’s degree in a quantitative field (Data Science, Statistics, Mathematics, Computer Science, Engineering) or equivalent practical experience.

Experience and Skills:

Required:

• 3–5 years of hands-on experience in data science, analytics, or a related field

• Proficiency with at least one programming language commonly used in data science (e.g., Python or R). Strong SQL skills.

• Solid foundation in statistics and experimental design (hypothesis testing, regression, time-series analysis) and feature engineering for sensor/data-rich environments.

• Experience building and evaluating predictive models (e.g., predictive maintenance, energy forecasting, anomaly detection) and translating results into business actions.

• Ability to translate operational problems into data solutions and communicate results clearly to both technical and non-technical audiences.

• Familiarity with data visualization tools and storytelling (e.g., Tableau/Power BI/Looker or Python visualization libraries).

• Experience with version control (Git) and reproducible workflows; comfort with data pipelines and ETL/ELT concepts.

• Comfortable working in a collaborative, multi-functional environment and managing multiple priorities.

Preferred:

• Experience in facilities management and engineering, or real estate analytics is a strong plus.

• Experience with building management systems (BMS/EMS), CMMS/EAM, CAFM systems (e.g., Planon, Archibus, IBM TRIRIGA), or GIS/CAD data integration.

• Familiarity with cloud platforms (AWS/GCP/Azure) and big data tools; exposure to ML lifecycle concepts (model monitoring, deployment, MLOps basics).<