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.
- 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 actual 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.
YOU MUST HAVE
- Bachelor’s degree in Engineering, Computer Science, Statistics, Actuarial Science, Economics or a related quantitative discipline.
- Minimum of 4 years of experience in data analysis, analytics, data science or data engineering.
- Strong SQL, with the ability to write and troubleshoot complex queries independently.
- Working proficiency in Python for data work, including cleaning, transformation and analysis with libraries such as pandas and numpy.
- Hands-on experience building production data pipelines ingestion, transformation and quality checks using Spark or an equivalent distributed processing framework.
- Experience building reports and datasets in Power BI or a comparable business intelligence tool, including the data model behind the visuals.
- Practical experience building and validating at least one predictive or statistical model that was used by a business, including how its accuracy was measured.
- Demonstrated experience delivering to recurring reporting deadlines with a high standard of accuracy, working independently on assigned areas.
- Working proficiency in English and Spanish, written and spoken.
WE VALUE
- Experience with Databricks specifically, including Delta tables, ML Flow and SDP/Pipelines.
- Depth in statistics and predictive modeling — regression, classification, gradient boosting or time-series forecasting — including how model performance is validated and communicated.
- Experience working in Microsoft Azure.
- Familiarity with version control and collaborative development practice, such as Git.
- Background in manufacturing, supply chain, procurement or finance.
- Curiosity about the business behind the data, and the instinct to ask why a number moved rather than only report that it did.
- Attention to detail, including the judgment to recognize when a result looks wrong and to check it before it is published.
- Interest in working across data engineering, analytics and data science rather than specializing early, and ambition to progress into Data Science or Data Engineering roles.
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