Program Data Analyst - Digital Enablement, Sustainable Infrastructure

What you will do Johnson Controls Sustainable Infrastructure Digital Enablement team is seeking a Program Data Analyst who is comfortable building structure out of messy, unstructured project data and turning it into something the business can act on. The ideal candidate moves easily between preparing large datasets, answering analytical questions under real deadlines, evaluating where AI can genuinely help, and building working prototypes that prove an idea before anyone commits budget to it.

The Program Data Analyst supports the Digital Enablement team by processing project data, performing analysis and research, evaluating and implementing AI use cases, and developing proofs of concept. This role works directly with project teams, engineering, sales, and functional leaders, consolidating information from multiple systems and sources into accurate, timely outputs that support delivery and decision-making.

The successful candidate is a self-starter who can manage several concurrent requests, document a method well enough that someone else can repeat it, and say clearly when an approach will not work. How you will do it Project Data Processing Support Extract, clean, and normalize project data from documents, PDFs, scanned files, and spreadsheets. Collect, validate, and consolidate data from multiple business systems and reporting sources.

Run quality checks, document assumptions and exclusions, and flag gaps before they reach a deliverable. Build repeatable processing routines and structured outputs that reduce manual rework. Maintain version control and file organization so the current source of truth is unambiguous. Analysis and Research Analyze prepared datasets to answer defined questions, including comparisons, benchmarking, trend analysis, and estimates.

Research external data sources, tools, and vendor capabilities, and summarize what is usable, what it costs, and where the limits are. Produce recurring and ad hoc reports and create visualizations that communicate findings clearly to leadership. Present findings and recommendations to project teams and cross-functional stakeholders. AI Use Case Evaluation and Implementation Identify candidate AI use cases with business owners and assess feasibility, data readiness, and expected value before any build commitment.

Implement approved use cases using approved AI services and tooling, working within company data handling and governance requirements. Validate output quality against a documented baseline and measure the effort saved against the manual process it replaces. Track accuracy and adoption after release, and rework or retire use cases that do not hold up in day-to-day use. Maintain a running view of the AI use case pipeline so the team can see what has been tried, what worked, and what was set aside.

Proof-of-Concept Development Build small working prototypes using scripting, cloud services, and API integrations to test a proposed automation or digital tool. Scope each proof of concept with the requesting stakeholder up front: the question it answers, the data it needs, and the success criteria. Demonstrate results and drive a documented go or no-go decision, including the effort, cost, and risk of taking it further.

Hand off validated prototypes to IT or a development partner with enough documentation to rebuild to production standards. Cross-Functional Collaboration Work closely with project teams, engineering, sales, IT, and functional leaders to understand what each request actually needs to answer. Manage several concurrent requests against project deadlines and escalate early when one is at risk. Explain methods, assumptions, and limitations in plain language to audiences that are not analytical.

What we look for Required Master's degree in engineering, data science, computer science, information systems, or a related technical field. Demonstrated ability to process large, inconsistent datasets end to end, including extraction from documents and reconcili