Celanese Corporation is a global chemical leader in the production of differentiated chemistry solutions and specialty materials used in most major industries and consumer applications. Our businesses use the full breadth of Celanese's global chemistry, technology and commercial expertise to create value for our customers, employees, shareholders and the corporation. As we partner with our customers to solve their most critical business needs, we strive to make a positive impact on our communities and the world through The Celanese Foundation. Based in Dallas, Celanese employs approximately 13,000 employees worldwide and had 2023 net sales of $10.9 billion. For more information about Celanese Corporation and its product offerings, visit www.celanese.com.
Key Responsibilities:
Document best practices of ML to be followed by the IT Development and
Business teams for indefectible ML systems and for the efficient flow of
projects.
Identify challenges faced by data analysts and data scientists and develop
solutions to integrate into the existing platform.
Possess good problem-solving skills and knowledge of machine learning
techniques for solving a range of problems.
Possess knowledge of neural network programs and natural language
processing.
Write programs and algorithms to extract insights from large data sets.
6+ years of experience as Machine Learning Engineer with Optical
character recognition.
Proficient in contextualizing PDF documents with Enterprise systems like
SAP, TrackSys, Aveva PI etc.
Implement AI and ML solutions and establish system operations and
maintenance structures.
Provide strategic and operational advice to the stakeholders regarding
AI/ML infrastructure to serve current and future needs.
Transform data science prototypes and optimise machine learning
solutions for scalability and deployment into production.
Design dynamic ML models and systems which have the ability to train,
retrain, and untrain themselves whenever required.
Periodically evaluate the ML systems and ensure that end solutions are in
alignment with corporate and IT strategies.
Evaluate the variations in data distribution that affects the model
performance.
Visualise data for insights and perform statistical analysis, applying new
insights to improve the model.