As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for designing, developing, and implementing data driven solutions for all Honeywell business groups and functions
Job Summary
As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for designing, developing, and implementing data driven solutions for all Honeywell business groups and functions.
The Advance Data Scientist role is expected to work within Honeywell to build and deploy innovative AI/ML models and solutions that generate new business growth and value.
In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package.
Matching Summary
As an Advance Data Scientist, you will join a high-performing, global team, and be responsible for designing, developing, and implementing data driven solutions for all Honeywell business groups and functions.
Skills & Requirements
Must-have
Python machine learning techniques
ML applied on processes, systems, hardware
Distributed storage and compute tools (Spark)
Cloud ML model deployment (AWS, Azure, GCP)
Deep learning frameworks (PyTorch, Tensorflow, Keras)
Containerized microservices and orchestrated batch runs
Nice-to-have
MLOPS best practices and implementations
LLM and Natural Language Processing models
Working with remote and global teams
Results driven with a positive can-do attitude
Key Requirements
Bachelor’s degree in computer science, Engineering, Applied Mathematics or related STEM field
Minimum of 4 years of full time Data Science prototyping experience (Python)
Minimum of 4 years of full time Machine Learning experience
Minimum of 4 years of experience with distributed storage and compute tools
Minimum of 4 years' experience developing and deploying machine learning models on cloud platforms
Minimum of 4 years of experience in deep learning frameworks
Minimum of 4 years' Experience with designing, building models and deploying pipelines to production