Caring for the world, one person at a time... inspires and unites the people of Johnson & Johnson. We embrace research and science - bringing innovative ideas, products and services to advance the health and well-being of people. Employees of the Johnson & Johnson Family of Companies work with partners in health care to touch the lives of over a billion people every day, throughout the world. We have more than 260 operating companies in more than 60 countries employing approximately 134,000 people. Our worldwide headquarters is in New Brunswick, New Jersey, USA.
In the era of modern finance, analytics and automation is becoming a 'must have', in that sense, you will join a group in charge of those two areas within the global Finance Hospital Medical Devices PIC (Principal, Inventory & Costing) CoE. Driving efficiencies, generating insights and enabling finance SMEs to create add value through new technologies is at the core of the Finance Automation & Analytics group. As part of the Automation & Analytics team you will deliver analytics insights & business benefits across over 10 sub franchises enabling all facets of the business to make more data driven decisions driving business benefits.
Main Responsibilities
As a Finance Supply Chain Data Scientist, you will be a key member of the Automation & Analytics team, leveraging your knowledge of data analysis, machine learning modelling, technical system and project management skills to grow a data-centric culture, as well as scope and design new solution to transform business challenges into finance D&A opportunities.
- Participate in Data Science projects, from project evaluation to project delivery. In particular: detect good opportunities, Project assessment, clear vision of end-to-end data processing pipeline
- Develop new projects on cloud platform
- Work in close relation to other business partners (data engineers, data analysts or machine learning engineers) to build optimal solutions
- Build and continuous testing of ML models. Keep track of model changes using MLOps techniques
- Deliver support to the customer to ensure good integration of the project to SMEs daily use. Post-production follow-up
- Continue the development of an ongoing forecasting project
- Set up good practices
- Mentor SME on how to use and store data properly