AspenTech is looking to build the next generation of Asset Performance Monitoring AI by developing ground-breaking software solutions leveraging machine learning, AI, and cognitive computing.
Requirements
- Experience with machine learning algorithms (regression, semi-supervised learning, deep learning, reinforcement learning, time series analysis, predictive modeling, data mining, cognitive computing)
- Experience in Python programming, including data science specific packages, such as Pandas, Numpy, TensorFlow, PyTorch, Scikit-Learn etc.
- Experience in C++ or C
- Experience with LLM, generative AI and agentic AI workflow
- Experience delivering AI/machine learning applications to end-users
- 5+ years of experience in data analysis and software development/programming
- History of coming up with innovative and creative quantitative solutions, demonstrated through practical business impact.
Responsibilities
- Design and develop new machine learning and AI applications for the manufacturing and process industries
- Investigate new and developing technologies as they appear in industry and academia and determine how to leverage these new technologies into our software applications
- Contribute to Emerson’s intellectual property footprint in the space of AI
- Evangelize the capabilities of machine learning and artificial intelligence and represent Emerson as a subject matter expert at select venues
- Contribute to the roadmap of AI research and take the technical lead in AI research and development projects
- Leverage your skills and passion for machine learning, AI and cognitive computing to drive Emerson’s Asset Performance Monitoring strategy by developing ground-breaking software solutions
- Playing with data, in all its forms, including data mining, mathematical modelling, cognitive computing, expert systems, and emerging LLM-based and agentic AI techniques
Other
- MS in Science or Engineering, PhD preferred, with a strong quantitative focus, such as AI, Statistics, Computer Science, Data Science, or a related quantitative field.
- Track record of academic publications related to data science, AI and/or process optimization.
- A footprint of public code represented by e.g., a GitHub stack and contributions to open-source packages, is preferred.
- Problem-solving ability and attention to details
- Excellent interpersonal, communication, writing, and presentation skills
