This role pays more than
64%
of similar roles. Most pay
$126,800–$191,250
— the shaded band above.
At the midpoint, this role pays about
$163k
versus about
$159k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 42 roles with salary data.
Senior Data Scientist
The Senior Data Scientist joins the MedTech Surgery team to advance the service data strategy for connected medical device platforms. This role involves developing scalable data models, analytics pipelines, and AI-enabled insights to improve device reliability, service operations, and customer outcomes. The individual will collaborate with cross-functional teams to define telemetry requirements, design analytical structures for device logs, and build predictive models for failure prediction and root cause analysis. Key technical requirements include advanced proficiency in Python and SQL, experience with Apache Spark or Databricks, and expertise in cloud environments like AWS or Azure. The role focuses on the medical device domain, specifically addressing the challenge of managing high-volume machine log data and telemetry to enhance surgical technologies while ensuring compliance with regulatory standards such as FDA and EU MDR within a regulated healthcare environment.
What does a Data Scientist earn in California?
Median $208978 from 66 postings across 27 companies.
Execute a service data strategy aligned with Service Operations priorities and the Digital Innovation Roadmap.
Design and maintain scalable analytical data models for device telemetry, logs, and customer interaction data.
Translate service use cases into structured logging requirements, telemetry specifications, and data contracts for engineering teams.
Develop predictive and diagnostic models for device performance monitoring, failure prediction, and root cause analysis.
Apply machine learning and statistical methods to large-scale datasets to optimize service resources.
Integrate and normalize service data across enterprise systems including ERP, CRM, and various service platforms.
Define service KPIs and develop dashboards that translate complex data into actionable business recommendations.
Ensure all analytical solutions comply with auditability, cybersecurity, privacy, and regulatory requirements.
What we're looking for
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or a related technical field.
5+ years of experience in data science, data engineering, analytics, or a related role involving large-scale datasets and distributed processing systems.
Advanced proficiency in Python and SQL for data processing, statistical analysis, machine learning, and query optimization.
Hands-on experience with Apache Spark or comparable distributed data frameworks and notebook-based environments like Databricks.
Experience designing analytical data models including relational, dimensional, and time-series structures.
Experience developing governed data pipelines and analytics-ready datasets in cloud environments such as AWS or Azure.
Strong communication skills to translate business use cases into technical requirements and explain complex concepts to non-technical audiences.
Experience with IoT, medical devices, or regulated industries including knowledge of FDA, EU MDR, GxP, and data privacy (preferred).