As a Sr Advanced Data Analyst here at Honeywell, you will play a crucial role in providing expertise in driving data-driven insights and analytics to optimize operational efficiency and improve decision-making within the Integrated Supply Chain (ISC) organization. You will collaborate with cross-functional teams and leadership to address business challenges and develop analytical solutions. You will use your mastery of data analytics to develop and implement data analytics strategies to drive continuous improvement. Your ability to translate complex data into actionable insights will be key in ensuring data integrity and accuracy at Honeywell.
In this role, you will impact decision making in the business, optimize processes, increase cost savings, and improve customer satisfaction. As a subject matter expert in data analytics, you will provide coaching for other members of the ISC analytics team to help in their skill growth.
ResponsibilitiesKey Responsibilities- Analyze and interpret complex data sets to provide actionable insights
- Collaborate with crossfunctional teams to address business challenges and develop analytical solutions
- Develop and implement data analytics strategies to drive continuous improvement
- Ensure data integrity and accuracy through thorough analysis and validation
- 5+ years of experience in data analytics
- Strong analytical and problem-solving skills, with the ability to translate complex data into actionable insights
- Proficiency in data analytics tools and programming languages (e.g., Python, R, SQL, etc.)
- Experience with data visualization tools (e.g., Tableau, Power BI, etc.)
- Bachelor's degree in a relevant field (e.g., Data Science, Analytics, Engineering, etc.)
- Strong leadership skills and the ability to effectively influence and coach others
- Proven track record of driving data-driven decision-making and delivering measurable business results
- Experience in supply chain analytics or related field
- Knowledge of advanced analytics techniques (e.g., machine learning, predictive modeling, etc.)
- Experience with data governance and data quality initiatives
