Sr Data Analytics Supervisor

Job Description

Partnering with channel leaders, sales teams, business leadership, and cross-functional stakeholders, you will develop and implement analytics, artificial intelligence (AI), and business intelligence solutions that enhance channel partner effectiveness, improve inventory health, strengthen market and channel intelligence, and accelerate business growth. Your ability to translate complex channel, customer, inventory, and market data into actionable insights will be critical in identifying growth opportunities, improving partner tiering and performance, enhancing forecast accuracy, optimizing inventory investments, and driving revenue growth. As a subject matter expert in analytics, you will champion data-driven decision-making across the organization while coaching and developing other analytics professionals to build a high-performing analytics capability.

Responsibilities

Key Responsibilities

• Develop and implement channel sales analytics strategies, frameworks, and governance processes to support business growth and operational excellence.
• Lead channel partner tiering and segmentation initiatives, leveraging data to identify growth opportunities, optimize partner investments, and improve channel performance.
• Drive inventory analytics and management programs to improve inventory health, minimize excess and obsolete inventory, and support demand planning and forecasting efforts.
• Develop channel intelligence capabilities by integrating sales, market, customer, and competitive data to generate actionable business insights.
• Lead the adoption of Artificial Intelligence (AI) and advanced analytics solutions to improve forecasting accuracy, partner recommendations, anomaly detection, and decision-making effectiveness.
• Collaborate with Sales, Channel Management, Supply Chain, Finance, and IT teams to identify business requirements and translate them into scalable analytical solutions.
• Design and maintain executive dashboards, KPI scorecards, and reporting frameworks that provide visibility into channel performance, inventory trends, and market opportunities.
• Ensure data quality, accuracy, governance, and consistency across channel sales and inventory data sources.
• Drive continuous improvement initiatives by identifying trends, risks, and opportunities through advanced analytics and predictive modeling.
• Stay current with industry trends, best practices, AI technologies, and emerging analytics capabilities to enhance Honeywell's channel analytics strategy.
  
  

Qualifications

YOU MUST HAVE

• 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.)
  
  

WE VALUE

• 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
  
  

About Us

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world's most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector's transition from automation to autonomy.