Data Scientist / AI Engineer (Temporary) (Miguel Hidalgo)

You will join our Mexico City office as a Data Scientist/AI-Engineer for a ~6‑month full‑time engagement, working directly with our consulting team and client across North America and Europe. The ideal candidate is a recent graduate with strong analytical and technical skills who is interested in applying data science, machine learning, and AI to real business problems. This role is designed for someone who is comfortable working with data, eager to learn quickly, and capable of combining technical analysis with clear communication.

Important Notes

This position is in‑person and based in Mexico City.

Application deadline: July 8th, 2026.

Immediate availability.

Responsibilities

• Support the development, testing, and refinement of analytical and AI‑enabled models.
• Assist in exploring advanced AI techniques, including neural networks, graph‑based models, large language models, retrieval‑augmented generation, and synthetic data generation.
• Work with financial, operational, transactional, text‑based, and external datasets to support client projects.
• Work with consultants to translate technical topics into clear business insights.
• Identify and help implement potential solutions for specific client problems or challenges.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Engineering, Mathematics, or a related quantitative field.
• Recent experience in AI and Data Science projects (academic or professional).
• Understanding of machine learning concepts and proficiency in Python.
• Strong communication skills, ability to explain technical findings in simple terms.
• Strong analytical and problem‑solving skills.
• High attention to detail and ability to work in a fast‑paced consulting environment.
• Familiarity with SOTA techniques and academic investigation.
• Fluent in English.

Preferred Technical Skills

• Experience with common Python data science libraries, including pandas, NumPy, scikit‑learn, matplotlib/Plotly/seaborn.
• Exposure to neural networks and deep learning frameworks such as TensorFlow or PyTorch.
• Interest in graph‑based machine learning, including graph neural networks, network analysis, relationship mapping, or entity‑link analysis.
• Familiarity with AI and data science applications in finance, financial services, risk management, transaction analysis, or business decision‑making.
• Exposure to large language models, prompt engineering, retrieval‑augmented generation, or other LLM‑enabled workflows.
• Experience working with structured and unstructured data, including tabular datasets, text data, documents, and external data sources.
• Familiarity with synthetic data generation, data augmentation, or techniques for working with limited, incomplete, or sensitive datasets.
• Experience working with APIs, data pipelines, automation scripts, or web scraping.
• Familiarity with Git, GitHub, or other version‑control workflows.
• Ability to document code, explain model outputs, and communicate technical findings clearly to non‑technical audiences.