Sr. Data Scientist
Join the industry leader to design the next generation of breakthroughs
When you join Honeywell, you become a member of our global team of thinkers, innovators, dreamers and doers who make the things that make the future.
That means changing the way we fly, fueling jets in an eco-friendly way, keeping buildings smart and safe and even making it possible to breathe on Mars.
Working at Honeywell isn’t just about developing cool things. That’s why all of our employees enjoy access to -dynamic career opportunities across different fields and industries. We offer amazing opportunities for career growth with a world-class team of diverse experts.
Are you ready to help us make the future?
Join a team that is elevating our strategy to drive advanced analytics and visualization tools across the Commercial enterprise. In this role, Sr Data Scientist – Commercial, you will conduct research and analysis to extract knowledge and insights from high volume, high dimensional data to provide strategic insight into complex business problems.This will involve data preparation, modeling, analysis, and visualization techniques.Some technologies you may apply to generate actionable insights include statistical analysis, machine learning, operations research, simulation, and artificial intelligence.
Duties and Responsibilities
·Lead complex data science and analytics projects in support of the commercial organization.
·Provides technical guidance or business process expertise, technical leadership, coaching and mentoring to team members and stakeholders.
·Independently develop data science models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control and maintaining documentation of created applications.
·Work directly with customers and business partners to develop requirements and provide technical solutions through an analysis of business needs and pain points.
·Proactively assess current capabilities to identify areas for improvement, proposing solutions that align with core strategy and operation.
·Develop guidelines and standards for analytics, AI, and machine learning models, their deployment, and associated processes.
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