The Sr Advanced Data Scientist – AI & Advanced Analytics is responsible for designing, building, and deploying Honeywell Aerospace–specific AI solutions that drive measurable improvements in productivity, speed, and business outcomes across enterprise functions and business verticals.

This role operates at the intersection of advanced analytics, classical machine learning, and Generative AI (LLMs and agentic systems). The individual will work closely with business stakeholders, IT, and engineering teams to translate complex business problems into scalable AI-driven solutions, owning the full lifecycle from ideation to production deployment and support.

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Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

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As a Sr Advanced Data Scientist here at Honeywell, you will leverage advanced analytics and machine learning to drive business insights and innovation. You will develop predictive models and provide actionable recommendations.

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Experience & Capabilities

• 8–10 years of experience in data science, advanced analytics, or applied AI roles
• Strong experience analyzing complex datasets and defining mathematical and statistical models
• Hands-on experience with:
  • Classical machine learning models
  • Advanced analytics techniques
  • Generative AI, LLMs, and agentic AI systems
• At least 2 years of experience designing, developing, and implementing enterprise AI solutions across business functions or industry verticals
• Proven experience working across the full AI solution lifecycle, from ideation to production

Technology & Platform Experience

• Strong experience with cloud platforms, including:
  • Microsoft Azure
  • AWS
  • Google Cloud Platform (GCP)
• Hands-on knowledge of AI platforms such as:
  • Azure OpenAI
  • Azure AI / Foundry
  • Google Vertex AI
  • AWS Bedrock
• Strong analytics, data engineering awareness, and domain understanding to support business-driven AI solutions

Education

• Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, or a related field

Who Will Succeed in This Role

• Techno-functional experts with hands-on AI / ML implementation experience
• Individuals who can define, build, deploy, and scale end-to-end AI solutions
• Professionals who can bridge business problems and advanced analytics / AI techniques
• Leaders who consistently deliver tangible, measurable outcomes using analytics, advanced analytics, and AI

#AERO26

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Key Responsibilities

Use Case Identification & Business Partnership

• Partner with business leaders and domain experts to identify, shape, and solidify high-value AI and analytics use cases
• Translate business problems into analytical, statistical, and AI-driven solution approaches
• Define clear success criteria, KPIs, and measurable business outcomes for each initiative

Data Understanding & Analytical Modeling

• Lead data discovery, data assessment, and data readiness activities across structured and unstructured data sources
• Perform complex data analysis, statistical modeling, and mathematical formulation to support solution design
• Select and apply appropriate classical ML techniques, advanced analytics, and AI models based on problem context

AI / ML Solution Development

• Design and develop end-to-end AI solutions using:
  • Classical ML models
  • Advanced analytics techniques
  • Generative AI, LLMs, and agentic AI frameworks
• Build AI applications that integrate classical models and LLM-based components to solve real business problems
• Ensure solutions are scalable, secure, explainable, and enterprise-ready

MLOps & Production Deployment

• Drive MLOps practices, including model versioning, monitoring, retraining, and lifecycle management
• Partner with platform, cloud, and engineering teams to ensure robust production deployments
• Ensure reliability, performance, and compliance of AI solutions in enterprise environments

Technology Evaluation & Architecture Decisions

• Evaluate and recommend AI platforms, tools, and frameworks aligned with enterprise standards
• Make informed technology and architecture decisions across cloud and AI ecosystems
• Maintain strong understanding of AI platform capabilities, limitations, and trade-offs

Enterprise Integration & Adoption

• Design and deliver integrated AI solutions that work seamlessly with enterprise systems and workflows
• Drive ideation workshops and innovation events to help business teams understand and adopt AI solutions
• Support change management and adoption by ensuring solutions deliver clear, tangible business value

End-to-End Technical Ownership

• Own solution design, development, implementation, deployment, and ongoing support
• Act as the technical authority for assigned AI initiatives
• Mentor junior data scientists and contribute to building a strong AI engineering culture