Data Scientist
Nippon Paint Bukit Merah Full-time
NIPSEA Group is a global leader, providing innovative solutions in the paint and coatings industry. Headquartered in Singapore, with more than 134 NIPSEA companies spread throughout 22 geographical locations, the group is Asia Pacific’s No. 1 paint and coatings manufacturer in both production and sales revenue.
After more than 60 years of growth, NIPSEA Group has over 27,000 employees with 102 manufacturing facilities and operations, efficiently serving all aspects of the business, from production to customer satisfaction. With a focus on maximizing value to our customers, we push boundaries to deliver high quality solutions that work better for all our partners, tradesmen, and homeowners.
After more than 60 years of growth, NIPSEA Group has over 27,000 employees with 102 manufacturing facilities and operations, efficiently serving all aspects of the business, from production to customer satisfaction. With a focus on maximizing value to our customers, we push boundaries to deliver high quality solutions that work better for all our partners, tradesmen, and homeowners.
The NIPSEA Group’s arsenal of solutions for the industry covers Architectural, Industrial, Automotive and Marine Coatings, as well as a range of products beyond the world of paint and coatings. We have an unyielding drive to focus on customers, providing innovation that works best for all our stakeholders.
To support our data science and AI efforts, we are seeking a Data Analyst / Data Scientist who has strong knowledge and expertise in data science, AI, machine learning, deep learning, generative AI and agentic AI, has at least 1-3 years of experience with implementing data science and AI projects in a dynamic business environment.Strong fresh graduates will also be considered. Successful candidates will work in a highly collaborative environment with internal and external stakeholders across different functional areas and business units. Candidates who additionally have experience with paints and coatings, chemistry or materials science are preferred.
Roles & Responsibilities- Implement and manage data science and AI projects throughout their lifecycles to meet business and technical needs
- Train, validate, test and deploy data science and AI models using on-premise and cloud resource
- Extract, transform, load and visualize data from various sources across the company in different geographical regions
- Gather and translate business and technical needs from internal and external stakeholders into metrics that can be optimized by data science and AI models
- Collaborate closely with domain experts (e.g. chemists), IT engineers, software engineers, technology leaders and business leaders to ensure that data science and AI projects will meet business and technical needs
- Prepare presentations and reports on data science and AI project outcomes to facilitate management’s decision-making and strategic planning
- Keep abreast of latest developments and trends in data science and AI and implement innovative data science and AI models and best practices timely
- Enthusiastic about learning relevant domain knowledge of paints and coatings that is needed to successfully execute data science and AI projects
- Bachelor’s or master’s degree in computer science, AI, data science, electrical engineering, statistics, mathematics or related fields
- Strong knowledge and expertise in data science, AI, machine learning, deep learning, generative AI, agentic AI
- At least 1 – 3 years of experience with implementing data science and AI projects in a business environment. Strong fresh graduates will also be considered.
- Proficient in programming e.g. Python, SQL
- Proficient in using machine learning frameworks e.g. PyTorch, TensorFlow, Keras, scikit-learn, MLOps
- Capable of working independently as well as in teams
- Excellent communication skills for presentations and reports
- Proficient in written and spoken English
- Experience with paints and coatings, chemistry or materials science is a plus
- Able to travel occasionally as and when required.
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