(CEN) GLOBAL DATA SCIENCE INTERN
At a glance
Junior Data Science role at Cemex Central. San Pedro Garza García.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Make datasets dependable for analysis by cleaning and checking their quality
- Use exploratory analysis and visualizations to produce insights for decisions
- Frame business problems as hypotheses and apply statistical methods to assess them
- Prepare modeling inputs, then build and refine predictive or prescriptive solutions with machine learning or advanced AI
- Help move analytical solutions toward production through validation, documentation, and reproducible practices
What you bring
- You are in semesters five through eight of a listed degree program
- You can produce well-structured Python for analysis and modeling, and you know SQL
- You can communicate technical ideas in English and explain findings through clear analysis and visual outputs
- You bring strong analytical problem-solving, attention to detail, and the ability to collaborate in an agile team
Who this fits
This internship suits a student interested in applying data science to Supply Chain use cases, with guidance from the Global Data Science team. Work is based at Campus CX, lasts six months with possible renewal, and requires twenty-five to thirty hours per week.
From the employer
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.
Specific Requirements / Qualifications
Required
- 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.)
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