Data Scientist

AI-First. Future-Driven. Human-Centered.

At OpenText, AI is at the heart of everything we do—powering innovation, transforming work, and empowering digital knowledge workers. We're hiring talent that AI can't replace to help us shape the future of information management. Join us.

The Opportunity

The OpenText Functional Testing Family provides functional testing solutions enabling customers to test earlier and faster by delivering AI-driven test automation across an unparalleled range of technologies; on the most popular browsers, mobile devices, operating systems, and form-factors; from the cloud or on-premises; to deliver the speed and resiliency required to achieve automation at scale that is tightly integrated with an organization’s current DevOps toolchain.

We are seeking a talented Data Scientist to join our fast-paced, agile team focused on advancing our product in the Functional Testing Domain. In this role, you will lead the development of AI-driven features, combining Computer Vision and Large Language Models (LLMs) to deliver intelligent, scalable solutions. You’ll work closely with global teams and contribute to a modern SaaS-based platform.

What the role offers

• Design, develop, and maintain deep learning models for Computer Vision applications
• Drive the research and implementation of LLM-powered features from concept to production
• Collaborate with cross-functional teams across multiple international locations
• Contribute to the architecture and evolution of a SaaS-based product
• Write clean, efficient, and scalable code aligned with best software engineering practices
• Partner with stakeholders to identify opportunities for data-driven solutions
• Evaluate new data sources and techniques to improve model performance and accuracy
• Build tools and processes to monitor, validate, and optimize model outcomes

What you need to succeed

• Proven problem-solving skills and a strong analytical mindset
• Proficiency in Python and experience with statistical and data manipulation libraries
• Hands-on experience integrating and fine-tuning LLMs for real-world applications
• Solid understanding of data architecture and scalable data pipelines
• Familiarity with a range of machine learning techniques (e.g., clustering, decision trees, neural networks) and their practical trade-offs
• Excellent communication skills for effective collaboration across teams
• Eagerness to learn and adapt to new technologies and methodologies