Inetum
Hiring now
Remote · MexicoFull-timePosted 3 months ago
Inetum
ApplyInetum es un grupo internacional de consultoría digital ágil. En la era post-transformación digital, nuestro propósito es permitir que cada una de las más de 27,000 personas que forman parte de nuestro equipo se renueve continuamente, viviendo de forma positiva su propio flow digital.
Con presencia en 26 países, promovemos trayectorias profesionales flexibles, innovación local y un equilibrio saludable entre la vida personal y profesional. Además, Inetum ha sido reconocida como Top Employer, certificación que avala nuestro compromiso con el bienestar, el desarrollo profesional y la excelencia en la gestión del talento.
Compromiso con la igualdad
En Inetum, promovemos un entorno de trabajo inclusivo y equitativo. Se tendrán en cuenta todos los candidatos, independientemente de su género, identidad, orientación sexual, edad, origen étnico, discapacidad u otras condiciones. Las decisiones de contratación se basan únicamente en las habilidades, competencias y valores alineados con nuestra cultura organizativa.
Nota de transparencia:
En nuestro proceso de selección utilizamos herramientas de inteligencia artificial para realizar un prefiltrado de perfiles. Estas herramientas se emplean únicamente como apoyo para el análisis y no toman decisiones de contratación, las cuales siempre son validadas por nuestro equipo de reclutamiento.
Si consideras que tu postulación fue evaluada de manera incorrecta, puedes escribirnos a:
reclutamiento.mx@inetum.com
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.
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.
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