BASF Coatings Technology (Shanghai) Co., Ltd. (BCTS) is founded in 2023 in Shanghai, China. The company specializes in providing global customers with a high-quality range of innovative and sustainable automotive OEM and refinish coatings’ solutions, powered by advanced capacity of production, R&D and digitalization.
BCTS currently employs approximately 400 individuals. In addition to sales and marketing, the company operates a resin plant for automotive coatings industry, producing a range of raw materials of coatings, including acrylics, polyester, polyurethane, e-coat binder, and intermediate grinding resin. A global Digital Transformation Unit (DTU) and global / regional R&D facilities are also operated by BCTS. The R&D facilities consist of spray booths, drying chambers, simulation line laboratories, and physical testing laboratories, which are used to test, refine, and innovate intermediate and finished products related to automotive coatings. DTU analyzes prospective future business sectors for coatings industry to offer digital solutions.
Objectives:
You will work as a data scientist being in charge of the development and implementation of digital solutions that use statistics, mathematics, and machine learning to leverage value creation in our Development and Customization labs at OEM Coatings. This includes potential projects along the whole value chain from formulation throughout sample creation, application and testing.
Main Task:
- Drive data science and AI / ML projects that add tangible value to the operational processes in the labs and application centers.
- Lead data science PoC or project to enable data-driven decision making.
- Model in data-scare applications by making better use of expensive lab experiments via smart choices of models and experimental designs.
- Develop models that incorporate scientifically-motivated a priori information and principle uncertainty estimation.
- Leverage models to optimize products and processes to find better solutions.
- Deliver solutions ready to be integrated in digital platforms and address efficiency and effectiveness
- Stay aware of related methodological developments in statistics and data science. Bring in new techniques, or participate in the development of new approaches through internal projects.
- Provide support to colleagues who wish to use self-serve software tools for data analysis and design of experiments.
- Actively exchange knowledge, ideas, and solutions with colleagues from different regions.
Job Requirements:
- Master’s degree in statistics, mathematics, data science, chemical engineering, materials science, or other related fields or an appropriate combination of training and experience
- Hands-on experience with data science for materials and formulation research, including statistics, machine learning, AI, etc.
- Ability to understand and translate real world inputs to physical/mathematical models and derive algorithms and scripts to effectively address the challenges of the real processes (applied science)
- Data analytics skills: knowledge of statistical programming languages like R, Python, Julia etc.
- Practical experience with DOE methods, including factorial, D-optimal, mixture, and space-filling designs, utilizing tools like JMP, MODDE, Minitab, or R
- Deep knowledge in statistics, mathematics, and machine learning, covering topics such as linear regression, linear model selection and regularization, basis expansion and smoothing, mixed models, Bayesian inference, functional data analysis, optimization, and various machine learning and deep learning techniques
- Experience with other necessary tools in data engineering & integration, such as database query language like SQL and/or tools such as Data Bricks
- Software engineering skills would be beneficial, e.g. API deployment, containers, cloud app
- Good communication skills as well as fluency in spoken and written English
- A proactive team player with initiative and high accountability
- You are motivated by continuous learning of new models and techniques in data science and AI / ML
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