(CEN) GLOBAL DATA SCIENCE INTERN

Cemex; Monterrey; Presencial; MXN 67.000 - 134.000; A tiempo parcial

Descripción de la vacante

Cemex is seeking a Global Data Science Intern to contribute to end‑to‑end analytical solutions in the Supply Chain domain. You will work 25–30 hours weekly at Campus CX, starting Oct 2026 for 6 months with renewal options.

You will process data, perform EDA, build models, and help deploy insights with mentorship from the Global Data Science team. Strong Python/SQL skills and English communication are required.

Formación

• Strong analytical and problem solving skills.
• Proficiency in Python, with the ability to write clean, reusable, and well structured code for data analysis and modeling.
• Solid knowledge of SQL.
• Ability to communicate insights effectively through data storytelling, using clear analysis and visual outputs.
• Strong collaboration skills and the ability to work effectively within a team oriented, agile environment.
• High level of attention to detail and a structured approach to problem solving.
• English proficiency sufficient for technical and professional communication in a global team.
• Familiarity with version control tools (e.g., Git).

Responsabilidades

• Process, clean, and validate data, ensuring data quality and reliability for analytical and modeling purposes.
• Conduct exploratory data analysis to generate insights and support decision‑making through analytical outputs and visualizations.
• Formulate hypotheses, design analytical approaches, and apply statistical techniques to validate results.
• Perform feature engineering and data preparation to support predictive and prescriptive modeling efforts.
• Develop, evaluate, and iterate on predictive or prescriptive models using machine learning or other advanced AI techniques.
• Contribute to the full lifecycle of Data Science solutions, including documentation, validation, and preparation for production deployment.
• Apply Data Science and Software Engineering best practices, including reproducibility, version control, and clear technical documentation.

Conocimientos

Python

SQL

Data storytelling

Git

Team collaboration

English proficiency

Agile

Herramientas

Snowflake

Power BI

Tableau

Descripción del empleo

Name of the Project: Global Data Science Intern

Vice Presidency: Digital Enablement

Region: Central

Intern’s Location (Office): Campus CX

Eligible Semester(s): 5th - 8th semester

Careers of Interest: Data Science, Computer Engineering, Software Engineering, Mathematics, Actuarial, Industrial Engineering, Systems Engineering, Physics, Mechatronics, Artificial Intelligence Engineering

Schedule Availability: Between 25 and 30 hours per week

Period of Stay at CEMEX: 6 months, with the option of renewal

Tentative Start Date: October 2026

Project Objective

Actively contribute to the development of end‑to‑end analytical solutions for real business use cases in the Supply Chain domain, covering the full lifecycle from data exploration and modeling to production deployment and result consumption, with technical mentorship and guidance from the Global Data Science team.

Challenges for the Students

• Understanding and translating real business problems into data driven solutions.
• Working with complex, large scale datasets and ensuring data quality, consistency, and reliability.
• Generating tangible business value.
• Developing analytical solutions that go beyond experimentation, considering scalability, maintainability, and usability.
• Collaborating effectively within a multidisciplinary, global team and adapting to agile ways of working.
• Applying modern data science best practices, including reproducibility, documentation, and code quality.
• Learning and using an enterprise grade data science stack

Main Responsibilities And Activities To Be Carried Out

• Process, clean, and validate data, ensuring data quality and reliability for analytical and modeling purposes.
• Conduct exploratory data analysis to generate insights and support decision‑making through analytical outputs and visualizations.
• Formulate hypotheses, design analytical approaches, and apply statistical techniques to validate results.
• Perform feature engineering and data preparation to support predictive and prescriptive modeling efforts.
• Develop, evaluate, and iterate on predictive or prescriptive models using machine learning or other advanced AI techniques.
• Contribute to the full lifecycle of Data Science solutions, including documentation, validation, and preparation for production deployment.
• Apply Data Science and Software Engineering best practices, including reproducibility, version control, and clear technical documentation.

Required

Specific Requirements / Qualifications

• Strong analytical and problem solving skills, with the ability to break down complex business problems into structured analytical tasks.
• Proficiency in Python, with the ability to write clean, reusable, and well structured code for data analysis and modeling.
• Solid knowledge of SQL.
• Ability to communicate insights effectively through data storytelling, using clear analysis and visual outputs.
• Strong collaboration skills and the ability to work effectively within a team oriented, agile environment.
• High level of attention to detail and a structured approach to problem solving.
• English proficiency sufficient for technical and professional communication in a global team.
• Familiarity with version control tools (e.g., Git).

Nice to Have

• Familiarity with Snowflake or other analytical data warehouses.
• Knowledge of web applications development REST APIs, HTML and/or CSS.
• Knowledge or practical experience in simulation techniques (e.g., discrete‑event simulation, Monte Carlo simulation).
• Knowledge or practical experience in optimization methods, such as linear, integer, or nonlinear optimization, heuristics, or metaheuristics.
• Familiarity with optimization or simulation libraries/tools (e.g., OR‑Tools, PuLP, Gurobi, CPLEX, SimPy, or similar).
• Experience with data visualization tools such as Power BI or Tableau.
• Exposure to AI models beyond academic coursework.
• Experience working with enterprise grade data platforms or cloud based analytical environments. (Azure, AWS, etc.)