Lead Data Scientist

Honeywell-posted 3 months ago
Full-time
Hybrid • Atlanta, GA
5,001-10,000 employees
Merchant Wholesalers, Durable Goods
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Honeywell Connected Enterprise (HCE) is a significant business segment of Honeywell International Inc., focusing on providing innovative software solutions that enhance operational efficiency and drive digital transformation across various industries. HCE leverages advanced technologies such as the Internet of Things (IoT), cloud computing, artificial intelligence (AI), and data analytics to create integrated solutions that improve productivity, safety, and sustainability. We're looking for a new team member who is motivated by cracking tough challenges with data, trained in problem solving, and with an unending thirst for learning. As a Lead 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. You will work closely with application architects to integrate results into operational platforms.

  • Identify opportunities for new growth and efficiency based on data analyses and foster relationships with business team members.
  • Recommend innovative solutions using various data science methods including hypothesis testing and define the data acquisition strategy when required.
  • Lead the technical execution of data science projects directing daily work of junior data scientists and ensure overall project success.
  • Manage stakeholder relationships by presenting regular updates and final results to senior leadership of the customer organization.
  • Participate in defining and governing the analytics strategy for Honeywell, building out AI/ML capabilities of the Forge platform.
  • Promote data science methods and processes across functions.
  • Bachelor degree in computer science, Engineering, Applied Mathematics or related STEM field.
  • Minimum of 7 years of full time Data Science prototyping experience (Python) using machine learning techniques and algorithms in a commercial setup.
  • Minimum of 7 years of full time Machine Learning experience applied on top of processes, systems, and hardware in a commercial setup.
  • Minimum of 6 years of experience with distributed storage and compute tools (e.g. Spark).
  • Minimum of 6 years' experience developing and deploying machine learning models on cloud platforms (e.g. AWS, Azure, GCP).
  • Minimum of 4 years of experience in deep learning frameworks like PyTorch, Tensorflow, Keras.
  • Minimum of 4 years' experience with designing, building models and deploying pipelines to production using containerized microservices and/or orchestrated batch runs.
  • Master's degree in computer science, Engineering, Applied Mathematics or related STEM field.
  • PhD degree in Computer Science, Engineering, Applied Mathematics or related STEM field.
  • Experience with MLOPS best practices and implementations.
  • Experience with LLM and Natural Language Processing models.
  • Experience working with remote and global teams.
  • Results driven with a positive can-do attitude.
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