Data Scientist

• Collaborate with stakeholders to identify business requirements and expected outcomes.
• Identify and select relevant and credible data sources, and provide technical instructions to data analysts to collect, integrate, and mine data;
• Identify and analyze patterns. Design experiments, test hypotheses, and build models;
• Conducts advanced data analysis and complex algorithms to help stakeholders to discover and quantify leading information.
• Resolve stakeholders' problems through analytics and communicate results and methodologies through an iterative collaboration process.
• Partner with stakeholders to ensure the expected qualification and assurance of the information.
• Design and lead implementation of analytics pipelines and visualization by collaborating with other data/informatics teams.
• Scout, test, and recommend new analytics algorithms and models to the data analytics.
• Educate the organization on new analytical approaches to drive the acceleration of business decisions.
• Collaborate with other data scientists to develop the strategy of the data analytics competency and prioritize development needs.

Skill Requirements

• Advanced degree in chemical engineering and/or chemistry & materials science.
• More than 2 years of data analytics experience in manufacturing, operation, and chemical/mechanical processing data.
• Expertise in first-principles modeling (kinetics, thermodynamic, and physical models)
• Expertise in data analytics software and tools: Python, R, Spark, JSON.
• Expertise in Machine Learning and Deep Learning, e.g., classification, regression, clustering, feature engineering, and neural networks.
• Expertise in Statistics, e.g., time series, regression models, principal component analysis, and multivariate analysis.
• Strong business acumen with the ability to translate business problems into data analytics requirements and design data models and analytics pipelines to resolve the problems.
• Excellent communication and customer-facing skills with the ability to explain technical approaches and the rationale of findings in easy-to-understand terms for the business.
• Experience using deep learning and machine learning on manufacturing processing and/or IoT event detection will be preferred.
• Expertise in Text Data Analysis: semantic analysis, natural language processing, taxonomy.
• Proficiency in data architecture and data modeling using relational databases or graph databases.
• Development/Analytics Platform: Posit Connect, Azure Data Lake, Databricks, AspenTech, IP21, TrakSYS
• Other software tools: NoteJS, web service, and visualization tools (e.g., Power BI)
• Familiar with Cloud DevOps and software engineering methodology.