Machine Learning Engineer
Veröffentlicht:
23 Oktober 2024Pensum:
100%Vertrag:
Festanstellung- Arbeitsort:Johnson
Johnson & Johnson is currently seeking a Experienced Machine Learning Engineer, Advanced Analytics to join our Data Science and Digital Health organization located in India.
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
The R&D Data Science and Digital Health (DSDH) team within Johnson and Johnson innovative Medicine develops innovative technology solutions to gain efficiency in R&D.
Successful candidates will develop cutting-edge methodologies to drive global development decisions and positively impact the way we conduct trials for patients. We are looking for outstanding scientists whose responsibilities include:
- Develop machine learning and artificial intelligence solutions to accelerate and enable our R&D global drug development business processes
- Work in agile development teams to develop state of the art machine learning pipelines
- Contribute to patient modeling and trial simulations
- Implement and define execution strategies for individual AI and machine learning projects
- Process and transform data to impactful metrics maximizing productivity with artificial intelligence
- Closely partner with the data scientists to execute on the priorities, building a roadmap to deliver the projects from data feasibility to final presentation to senior cross-functional leaders
- Clearly articulate highly technical methods and results to partners to drive decision-making
- Build multi-modal machine learning pipelines using internal and external data such as clinical operations data, productivity data, real-world data, financial, and socio-demographic data