面議(經常性薪資達4萬元或以上) 新竹市東區 工作經歷不拘 6天前
• Partner directly with R&D teams to understand their scientific challenges, data, and experimentation workflows, then translate them into DoE and optimization studies on the Uncountable platform.
• Use the platform’s visualization, statistical, and machine-learning tools to explore experimental data, surface key drivers and relationships, and recommend the next best experiments.
• Onboard and coach R&D scientists on the platform — ensuring data is well-structured and models are configured to reflect their specific scientific context.
• Help scientists interpret model outputs, act on recommendations, and build lasting confidence in model-guided experimentation.
• Deliver measurable value: faster cycles, fewer experiments, and better-performing formulations and processes.
Preferred Qualifications
• M.S. or Ph.D. in a quantitative field such as Chemistry, Materials Science, Chemical Engineering, Physics, Data Science, or a closely related discipline.
• Familiarity with Bayesian optimization, design of experiments (DoE), or active-learning methods in applied settings.
• Exposure to product-development processes in materials, chemicals, or adjacent industries.
• Ability to build capabilities beyond a single platform — e.g., custom models with Python ML libraries, dashboards, or data-visualization tools.
• Prior experience with R&D data or experimentation platforms (Uncountable or similar) and integrating their outputs with other analytics.
展開