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Roles and Responsibilities
We’re looking for a new team member for our Global Finance analytics team 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, including Hadoop and NoSQL architectures.
The Lead Data Scientist role is expected to work within Honeywell to identify opportunities for new growth and efficiency based on data analyses and foster relationships with business team members by being proactive, displaying a thorough understanding of the business processes.
You will also be responsible for recommending innovative solutions by using various data science methods including hypothesis testing and also be responsible for defining the data acquisition strategy when required.
After influencing scope and prioritizing the analytics pipeline, you will lead the technical execution of data science projects directing daily work of junior data scientists and will be responsible for the overall success of the project.
This includes stakeholder management by presenting regular updates and final results to senior leadership of the customer organization.
You will also be expected to actively participate in defining and governing our analytics strategy for Honeywell building out AI/ML capabilities of our Forge platform and promoting data science methods and processes across functions.
You will report to the Data Science Site Leader in the Honeywell Industrial Analytics organization, part of the Connected Enterprise.
You must have:
Master’s degree in Computer Science, Engineering, Applied Mathematics or related field
Exposure to Finance domain and use cases in larger global enterprise setting 
Minimum of 5 years of Data Science prototyping experience (Python and/or R tool-stack) using machine learning techniques and algorithms such as as k-means, k-NN, Naïve Bayes, SVM, Decision Trees
Minimum of 5 years of Machine Learning experience of physical systems
Minimum of 4 years of experience with distributed storage and compute tools (e.g. Hive and Spark)
Minimum of 2 years of experience in deep learning frameworks like PyTorch, Keras
Experience with designing, building models and deploying pipelines to production using containerized microservices and/or orchestrated batch runs

We value: 

PhD degree in Computer Science, Engineering, Applied Mathematics or related field
Experience with Natural Language Processing models
Experience with Streaming Analytics (i.e. Spark Streaming)
Experience with Recurrent Neural Network architectures
Experience with Image Analytics
Experience with SQL
Experience with Tableau
Experience working with remote and global teams
Results driven with a positive can-do attitude

YOU MUST HAVE

• Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering or mathematics

WE VALUE

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