Honeywell

21 hours ago

Sr Data Science Manager

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 16 hours agoStatus: Live

Job Summary

Lead data science and engineering initiatives that drive ideation and savings outcomes. Own the data model, production data pipelines, and reporting/analytics platform used across ~10 source systems. Build and monitor predictive models for implementation likelihood, savings realization, and forecasting. Manage the monthly/quarterly productivity and savings reporting cycle, collaborate with senior leaders on KPI root-cause analysis, and coach a team of data engineers, analysts, and data scientists.

Location: Monterrey

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Own the data model behind ideation and savings so metrics can be decomposed and models can be trained consistently across regions and sites.
•Design, deliver, and operate production data pipelines and the platform supplying reporting and analytics from approximately 10 source systems, including data quality and access governance.
•Build and own predictive models for ideation and execution likelihood, savings realization, project delay risk, and year-end attainment forecasting, including validation and monitoring.
•Own the monthly savings and productivity reporting cycle end to end, including data validation, reconciliation with Finance, and on-time leadership reporting.
•Lead, coach, and grow a team of data engineers, analysts, and data scientists, setting data engineering standards and managing hiring, capacity planning, and performance.

Key Requirements

•Minimum of 10-12 years of experience in data engineering, analytics, or data science, including at least 5 years directly leading technical teams with hiring, development, and performance management.
•Hands-on experience designing and operating production data pipelines on a cloud platform with cost management, with working proficiency in SQL and Python.
•Experience building and deploying predictive models in production (classification, regression, gradient boosting, or time-series forecasting) with validation against outcomes and monitoring after deployment.
•Experience designing and deploying data models or semantic layers across a business, including consistent definitions and dimensions.
•Fluency in English and Spanish, written and spoken, and demonstrated ability to present KPI performance and recommendations to senior leaders.

Experience:10-12 yearsData engineeringAnalyticsPredictive modelingManufacturingSupply chain

Education:Bachelor's in Engineering, Computer Science, Information Systems or a related discipline

Skills:LeadershipMentoringProblem-solvingCommunicationStakeholder management

Languages:EnglishSpanish

Tech Stack:SQLPythonMicrosoft AzureDatabricksPower BIPostgreSQLMendixLow-code

Company Brief

Honeywell

Global diversified technology and manufacturing company providing aerospace systems, building technologies, performance materials, and safety & productivity solutions for industrial, commercial, and consumer markets.

Industry: Conglomerates & Holding Companies

Company Size: Enterprise (1,001+ employees)

Revenue: USD 25B to 50B

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Charlotte, United States

Founded: 1906

Glassdoor: 3.8

Website LinkedIn Glassdoor

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Honeywell

21 hours ago

Sr Data Science Manager

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 16 hours agoStatus: Live

Job Summary

Location: Monterrey

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Own the data model behind ideation and savings so metrics can be decomposed and models can be trained consistently across regions and sites.
•Design, deliver, and operate production data pipelines and the platform supplying reporting and analytics from approximately 10 source systems, including data quality and access governance.
•Build and own predictive models for ideation and execution likelihood, savings realization, project delay risk, and year-end attainment forecasting, including validation and monitoring.
•Own the monthly savings and productivity reporting cycle end to end, including data validation, reconciliation with Finance, and on-time leadership reporting.
•Lead, coach, and grow a team of data engineers, analysts, and data scientists, setting data engineering standards and managing hiring, capacity planning, and performance.

Key Requirements

•Minimum of 10-12 years of experience in data engineering, analytics, or data science, including at least 5 years directly leading technical teams with hiring, development, and performance management.
•Hands-on experience designing and operating production data pipelines on a cloud platform with cost management, with working proficiency in SQL and Python.
•Experience building and deploying predictive models in production (classification, regression, gradient boosting, or time-series forecasting) with validation against outcomes and monitoring after deployment.
•Experience designing and deploying data models or semantic layers across a business, including consistent definitions and dimensions.
•Fluency in English and Spanish, written and spoken, and demonstrated ability to present KPI performance and recommendations to senior leaders.

Experience:10-12 yearsData engineeringAnalyticsPredictive modelingManufacturingSupply chain

Education:Bachelor's in Engineering, Computer Science, Information Systems or a related discipline

Skills:LeadershipMentoringProblem-solvingCommunicationStakeholder management

Languages:EnglishSpanish

Tech Stack:SQLPythonMicrosoft AzureDatabricksPower BIPostgreSQLMendixLow-code

Company Brief

Honeywell

Industry: Conglomerates & Holding Companies

Company Size: Enterprise (1,001+ employees)

Revenue: USD 25B to 50B

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Charlotte, United States

Founded: 1906

Glassdoor: 3.8

Website LinkedIn Glassdoor