Senior Data Scientist - Power BI & AI
Johnson & JohnsonAbout the role
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 jnj.com
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Data Analytics & Computational SciencesJob Sub Function:
Data ScienceJob Category:
Scientific/TechnologyAll Job Posting Locations:
New Brunswick, New Jersey, United States of AmericaJob Description:
We are searching for the best talent for Senior Data Scientist - Power BI & AI to be in New Brunswick, NJ.
About Innovative Medicine
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine
Purpose:
We are hiring a Senior Data Scientist with 7+ years of experience building scalable, production-grade analytics solutions that turn complex financial and operational data into actionable business insights.
This is primarily a senior BI and analytics engineering role embedded within a data science team. The ideal candidate brings deep expertise in Power BI, semantic modeling, and analytics engineering, with the ability to integrate data science outputs such as forecasts and predictions into governed, high-performance BI products.
You will work across the analytics lifecycle, from data transformation and semantic model design to dashboard development, performance tuning, and Copilot-enabled analytics experiences. You will partner closely with business stakeholders, data engineers, and data scientists to deliver scalable, enterprise-ready decision-support solutions.
This role offers the opportunity to help shape next-generation BI by combining trusted data models, modern Microsoft Fabric capabilities, and AI-powered analytics experiences.
You will be responsible for:
Design, develop, and maintain enterprise-grade Power BI semantic models that integrate financial data and data science outputs to support decision-making.
Build and optimize trusted, analysis-ready datasets using SQL, PySpark, and modern data modeling practices.
Apply best practices in Power BI development, including DAX optimization, dimensional modeling, workspace governance, security, and performance tuning.
Define and enforce semantic modeling standards for scalability, usability, and consistency across BI solutions.
Optimize large-scale datasets using techniques such as Direct Lake, incremental refresh, and partitioning where appropriate.
Prepare data for AI and Copilot-enabled analytics experiences by simplifying schemas, setting up verified answers, and authoring domain-specific instructions.
Design and support Fabric-based analytics and agentic experiences that enable governed, natural-language access to curated business data.
Implement strong engineering practices, including version control, CI/CD, testing, documentation, and support for production BI assets.
Partner effectively with business stakeholders, data engineers, data scientists, and technology teams to define requirements, deliver solutions, and drive adoption.
Success in this role:
Improve the performance, scalability, and usability of enterprise BI solutions.
Increase adoption of governed semantic models and self-se
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