Description
As a Data Scientist II here at Honeywell, you will build the analytics, models and data pipelines that track productivity and savings across Honeywell Technologies. You will work alongside our senior data members on the monthly reporting cycle, the analysis that explains why performance is moving, and the data pipelines that feed both. This role is roughly half data engineering and half analysis and modeling: you will build and own the pipelines together with the data engineering team – as well as building analytics and models for business decision making.

Responsibilities
- Own the queries, datasets and reporting views that support the monthly savings and productivity reporting cycle and run the validation checks that must pass before the reporting is published.
- Develop and maintain data pipelines that bring data in from source systems and prepare it for reporting and analysis, working to the engineering standards set by the team.
- Investigate data quality issues raised by users, trace them back to the source,



and implement the fix or escalate it with a clear explanation of the cause.
- Build and maintain Power BI reports and datasets used by sites and regional leaders.
- Perform decomposition analysis: when a KPI is off target, break performance down by region, site, savings avenue, category and stage of the funnel to identify where the movement is coming from, and present the finding with a supported explanation.
- Contribute to the predictive models supporting ideation and execution: prepare and engineer the features, run exploratory analysis, build and test candidate models, and validate output against presente results.
- Respond to analysis requests from the business with clear, well-presented answers, and explain what the numbers mean rather than only what they are.
- Document datasets, definitions and logic so that analysis can be reproduced and maintained by others, and support less experienced team members in doing the same.

Qualifications
Y

📌 Data Scientist II (Monterrey)
🏢 Honeywell
📍 Monterrey