Honeywell
Website:
honeywell.com
Job details:
Overview
JOB DESCRIPTION
We are seeking an
Analytics Professional to drive innovative analytics and AI solutions in a long-cycle, B2B industrial business environment. In this role, you will blend
advanced data analytics with
emerging AI technologies to help our enterprise make smarter, faster decisions. You will develop insightful
Power BI dashboards (with robust data modeling and storytelling) and design
AI-powered workflows that automate and augment business processes. You’ll work in a complex enterprise context – collaborating with cross-functional teams (Sales, Finance, Marketing, etc.) – to deploy AI solutions that not only generate insights but also
take actions through integrations. The ideal candidate is equally comfortable building a DAX measure, crafting a prompt for a language model, or designing a user-friendly analytic app. This role is fast-paced and innovative, perfect for someone excited about applying
Generative AI, automation, and analytics in a real-world industrial setting.
Responsibilities
Key Responsibilities
- Data Visualization & BI: Develop and maintain interactive Power BI dashboards and reports, performing end-to-end data modeling, DAX calculations, and visual design to tell a clear story. Ensure analytics outputs are easy to interpret and drive action, with strong focus on data storytelling, usability, and impact on business KPIs.
- Exploratory Data Analysis: Conduct rigorous exploratory data analysis (EDA) on large datasets to uncover trends, validate hypotheses, and generate actionable insights. Clean, transform, and integrate data from various sources (SQL databases, APIs, spreadsheets) for analysis and AI model inputs.
- AI Solutions & Agentic Workflows: Design and implement AI-driven solutions that go beyond static reports. Leverage Generative AI and agent frameworks (e.g. Microsoft 365 Copilot’s Agent Builder) to create custom AI agents that can interact with enterprise systems and automate tasks via natural language instructions. Build multi-step agentic AI workflows using frameworks like LangChain/LangGraph for tool-assisted reasoning (e.g. an AI agent that analyzes data and triggers actions based on results). Ensure these AI solutions operate safely at enterprise scale, integrating with legacy systems, APIs, and following security/compliance guidelines.
- Automation & Integration: Utilize Power Automate and similar orchestration tools to streamline analytics processes and integrate AI outputs into business workflows. For example, build automated alerts, report refresh workflows, or trigger actions (emails, tickets, etc.) based on AI/BI insights. Combine Power Platform capabilities to ensure data and AI insights flow efficiently across systems (Power BI ↔ Power Automate ↔ other apps).
- Prompt Engineering & Model Usage: Craft effective prompts and instructions for generative AI platforms (OpenAI, Azure OpenAI, etc.) to get reliable outputs for business queries. Apply prompt engineering best practices – e.g. provide context, define roles, iterate prompts – to improve AI result quality. Continuously experiment with prompt and chain designs to refine the performance of AI agents and chatbots used in the team.
- Solution Design & UX: Apply sound design principles when building data/AI solutions. This includes designing intuitive user interfaces for analytics tools (e.g. well-organized Power BI UX) and ensuring AI agent interactions are user-friendly. Work closely with users to gather requirements and refine the UI/UX of analytics products, so that complex AI/analytics capabilities are presented in a clear, trustworthy manner.
- Cross-Functional Collaboration: Work with business stakeholders and domain experts (operations, sales, engineering, etc.) to identify opportunities where AI and analytics can add value in our long sales-cycle, industrial context. Translate business requirements into technical solutions – for example, partnering with sales ops to build an AI-assisted pipeline analysis dashboard. Communicate findings and demos to non-technical stakeholders in an understandable way, bridging the gap between data science and business.
- Continuous Learning: Stay updated on emerging tools and methods in AI and analytics. Proactively pilot new features (e.g. enhancements in Power BI, new Azure AI services, LangChain updates) and assess their fit for our use cases. Share knowledge and train teammates on using AI and BI tools effectively.
- Power BI Expertise: Proven ability to design, develop, and publish Power BI dashboards with complex data models. Strong command of DAX and Power Query for creating measures, calculated columns, and ETL transformations. Portfolio of impactful visualizations or data stories that drove decision-making is a plus.
- Data Analysis & SQL: Proficient in SQL for querying and manipulating data (joins, window functions, etc.). Solid understanding of data warehousing concepts and ability to blend data from multiple sources. Demonstrated skill in exploratory data analysis and statistical techniques to extract insights from data (experience with Python/R for data analysis is an advantage).
- AI/ML & GenAI Tools: Practical experience with generative AI or machine learning in an analytics context. Familiarity with LangChain and/or LangGraph frameworks to orchestrate LLMs in workflows, and exposure to vector databases or knowledge bases for retrieval-augmented generation (RAG) is a plus. Able to build and fine-tune simple AI/ML models or use pre-trained LLMs to analyze data and generate recommendations.
- Microsoft Copilot & Agent Builder: Experience (or strong interest) in using Microsoft 365 Copilot and its Agent Builder to create custom AI agents or copilots. Ability to configure agents with domain-specific knowledge and integrate them with Microsoft 365 apps (Teams, Outlook, etc.) to automate tasks. (For example, building a Copilot agent that summarizes weekly project reports and drafts email updates.)
- Workflow Automation: Hands-on experience with automation tools such as Power Automate (Flow) to create automated business processes, alerts, or integrations. Knowledge of how to connect AI outputs into automated workflows (e.g. an AI insight triggers a Power Automate flow that notifies stakeholders or updates a record). Any RPA (Robotic Process Automation) exposure is beneficial.
- Prompt Engineering & NLP: Strong understanding of how to interact with LLMs and AI APIs. Skilled at writing clear prompts/instructions to yield desired outcomes from AI (for instance, crafting a prompt for an AI agent to extract specific information). Awareness of limitations of AI (hallucinations, bias) and using techniques like few-shot prompting or chain-of-thought to improve results.
- Design and UX Sensibility: Broad knowledge of design mechanisms/principles – able to architect solutions that are modular, scalable, and maintainable. A good eye for UI/UX in analytic products: you prioritize clean, user-centric design in dashboards and ensure AI tools are intuitive to interact with. Experience collaborating with design teams or using wireframing tools to convey ideas is a plus.
- Soft Skills: Excellent problem-solving ability, analytical thinking, and creativity in solution development. Strong communication skills – capable of explaining technical concepts (analytics models, AI agent behavior, etc.) in simple terms. Proven ability to work in a cross-functional team and to engage with stakeholders at multiple levels. Self-driven and curious, with a passion for continuous learning in the AI/analytics space.
Qualifications
YOU MUST HAVE
- Education & Experience: bachelor's or master's degree in data science, Computer Science, Engineering, or related field. 3-6 years of hands-on experience in business analytics, BI development, or applied data science roles. Experience working in or with large B2B/industrial enterprises is highly valued.
- Power BI Expertise: Proven ability to design, develop, and publish Power BI dashboards with complex data models. Strong command of DAX and Power Query for creating measures, calculated columns, and ETL transformations. Portfolio of impactful visualizations or data stories that drove decision-making is a plus.
Data Analysis & SQL: Proficient in SQL for querying and manipulating data (joins, window functions, etc.). Solid understanding of data warehousing concepts and ability to blend data from multiple sources. Demonstrated skill in
exploratory data analysis and statistical techniques to extract insights from data (experience with Python/R for data analysis is an advantage).
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 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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