Descripción del empleo

We are looking for a Senior Data Scientist with a solid statistical background and

practical experience in data science projects in business environments. The

candidate must be able to frame complex problems as modeling problems,

execute rigorous research cycles, and deliver reproducible solutions that can be

integrated by engineering teams.

This role operates in close collaboration with an ML Engineering team. The

candidate is expected to have clear judgment about their responsibilities within

that ecosystem and the discipline to work with software engineering standards,

not just analysis standards.

Requisitos

What We Are Looking For

Technical Fundamentals

Advanced mastery of Python as the primary and sole development language.

Solid knowledge of data science and ML libraries: Scikit-Learn , XGBoost ,

LightGBM , Pandas , Polars , Statsmodels , SciPy .

Experience with deep learning models ( TensorFlow or PyTorch ) when the

problem justifies it.

Ability to work with data at scale: advanced SQL, PySpark for exploration

and transformation.

Access to and handling of data in cloud environments (GCS, Azure Blob

Storage).

Statistical Rigor

Experimental design and hypothesis testing applied to business problems.

Understanding of causality: not just correlation but the ability to distinguish and

apply appropriate techniques.

Robust model validation: beyond accuracy, business metrics, bias analysis, and

subgroup behavior.

Development Discipline

Professional use of Git as part of the usual workflow, not as a formality at

delivery time.

Organized and modular Python code: the candidate must produce deliverable

code, not just exploration notebooks.

Familiarity with experiment tracking tools (MLflow or equivalent) for

experiment traceability.

Ability to document models in a structured way: what it solves, with what data,

with what limitations.





Experience working under team standards: secure credential handling, data

versioning, project structure.

Judgment on AI

Responsible use of generative AI tools as assistants: with the critical ability to

review and validate what they produce.

Judgment to evaluate when agent systems or LLMs are the right tool and when

they are not.

Recommended Experience
Notes for the Search

The selection process includes a practical technical evaluation and review

of the candidate’s previous work.

Reasoning ability and judgment will be valued over code production speed.

We are not looking for profiles who use tools without understanding them: we

are looking for candidates who can justify their technical and statistical

decisions.

The candidate will work under engineering standards defined by the team —

willingness and ability to adopt them from the start of any project is expected.

More than 5 years in data science, statistical analysis, or applied research roles.

Documentable end-to-end projects: from problem definition to delivery of a

validated model.

Experience working with engineering teams (ML Engineers, Data Engineers) in

agile environments.

Work history in real code repositories (a shareable portfolio will be valued).

Academic Background

Master’s or Doctoral degree in: Mathematics, Statistics, Actuarial Science,

Physics, Computer Science, or related fields.

Experience in academic or applied research is a differentiator.

Información adicional

Lo que ofrecemos
Programas de capacitación continua y certificaciones.
Acceso a plataformas de aprendizaje y desarrollo profesional.
Cultura de innovación y colaboración.
Programas de bienestar físico y emocional.
Oportunidades de crecimiento en proyectos internacionales.
Reconocimiento y recompensas por desempeño.
Sueldo base
Prestaciones superiores a las de la ley
Seguro de vida
Seguro de Gastos Médicos Mayores
Vales de despensa
Esquema 100% nómina

📌 Data Scientist (Ciudad de México)
🏢 Inetum M´´exico
📍 Ciudad de México