Data Scientist

4 days ago

Irving Texas, United States Celanese International Corporation Full time
About Celanese International Corporation

Celanese International Corporation is a global leader in the production of specialty materials used across various industries and consumer applications. Our businesses leverage our chemistry, technology, and commercial expertise to create value for our customers, employees, and shareholders. We are committed to responsible management of the materials we create throughout their entire lifecycle and are expanding our portfolio of sustainable products to meet growing customer and societal demand.

Job Summary

The Data Scientist will support the Celanese Digitalization initiative, part of the Digital Innovation team. The ideal candidate will have experience collaborating with Data Engineers, Data Analysts, and Business teams to leverage large external and internal data sets for growth opportunities, optimization, insights generation, and trend identification.

Key Responsibilities
  • Work with stakeholders across the organization to identify opportunities for leveraging company data to drive business solutions.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
  • Assess the effectiveness and accuracy of new data sources/extracts and data gathering/merging techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
  • Stay up-to-date on the latest trends and technologies in AI/ML.
  • Coordinate with different functional teams to implement and maintain models.
  • Develop processes and tools to test, monitor, and analyze model performance.
  • Program languages such as Python, SQL, etc. Ability to manipulate data and draw insights from large datasets.
  • Machine learning frameworks and libraries specifically designed for working with LLMs, RAG, NLP, and text mining.
  • Large Language Models (LLMs), including but not limited to models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and their derivatives.
  • RAG techniques and their applications. Leverage RAG for enhancing the performance of language models by combining the power of retrieval and generative capabilities to provide more accurate, contextually relevant, and information-rich responses.
  • Practical application of LMMs in analyzing data that have multiple levels of correlation or non-constant variability.
  • Apply LMMs to complex datasets to account for both fixed and random effects, ensuring accurate data interpretation and decision-making.
  • Application with a wide array of machine learning techniques, including but not limited to clustering, decision tree learning, and artificial neural networks, and an understanding of their real-world advantages and limitations.
  • Working with deep learning tools including computer vision.
  • Advanced statistical techniques and concepts (regression analysis, distribution properties, statistical testing, etc.) and experience applying these techniques to data analysis and modeling.
  • Text mining, deep neural networks, autoencoders, hybrid models, and cloud computing.
  • Articulate complex concepts and findings to both technical and non-technical stakeholders. Collaborate effectively with cross-functional teams to drive projects to completion.
  • Work independently with experts from different fields, including chemical engineers, process engineers, and environmental scientists, to integrate diverse data sources and insights.
  • Code APIs and work with several languages like python, R, Java, JavaScript, etc.
  • Use web services like Azure Cognitive Services/ML, Databricks, etc.
  • Use Alteryx to analyze and manipulate data.
  • Analyze data from 3rd party Market Intelligence providers.
  • Use distributed data/computing tools like Spark, Hadoop, etc.
  • Visualize and present data for stakeholders using Power BI, SAP Analytics Cloud, etc.
  • Use SAP, Google Analytics, and Snowflake.
Qualifications
  • Required:
    • PH.D with 3 years of Data Science experience OR Masters with 5 years of chemical experience
    • Python
    • SQL
    • AI, Large Language Models (LLMs), including but not limited to models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and their derivatives.
    • Azure
    • Snowpark
    • Jupyter Notebooks
  • Desired:
    • Chemical Eng Background
    • Data Science Architecture
    • Experience working with R&D (Research & Development)

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