Sr Advanced Data Scientist

Join a team recognized for leadership, innovation and diversity

Senior Advance Data Scientist

Roles And Responsibilities

We’re looking for a new team member for our Finance Digital Operations team who is motivated by cracking tough challenges with data, trained in problem solving, and with an unending thirst for learning.

As a Senior advanced 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 other data scientists and Business Stakeholders to design, develop and deploy state of the art AI solutions. You will work in capacity as a tech lead and mentor the other team members to guide the objectives in the right direction.

You will also be responsible for recommending innovative solutions by using various data science methods including hypothesis testing, feature engineering, and be responsible for defining the data acquisition strategy when required.

This includes stakeholder management by presenting regular updates and 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 Global Data Science Site Leader in the Honeywell Industrial Analytics organization, part of the Connected Enterprise and work closely on defining best practices and driving AI strategy.

You Must Have

• Phd or 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 7 to 10 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 7 to 10 years of Machine Learning experience of physical systems
• Minimum of 4 to 6 years of experience with distributed storage and compute tools (e.g. Hive and Spark)
• Minimum of 7 to 10 years of experience in deep learning frameworks like PyTorch, Keras
• Experience with designing, building models and deploying models to Cloud Platforms like Azure and Databricks.
• Working knowledge and experience of implementing Generative AI in industry and other latest development in the field of Artificial Intelligence.
• A research mindset with a problem solving attitude is a MUST.