数据科学家
Data Scientist
我们寻找一位具有扎实统计背景并在商业环境中具备数据科学项目实践经验的高级数据科学家。候选人需要能够将复杂问题转化为建模问题，执行严谨的研究周期，并交付可由工程团队集成的可重复解决方案。
该职位与机器学习工程团队紧密合作。候选人需清楚了解自身在该生态系统中的职责，并具备按照软件工程标准而非仅分析标准进行工作的纪律性。
我们寻找的特质
技术基础
对 Python 的高级掌握，作为主要且唯一的开发语言。
对数据科学和机器学习库（Scikit-Learn、XGBoost、LightGBM、Pandas、Polars、Statsmodels、SciPy）有扎实的知识。
在问题需要时具备深度学习模型（TensorFlow 或 PyTorch）的经验。
具备处理大规模数据的能力：高级 SQL，使用 PySpark 进行探索和转换。
能够访问和处理云环境中的数据（GCS、Azure Blob Storage）。
统计严谨性
将实验设计和假设检验应用于业务问题。
理解因果关系：不仅关注相关性，还需具备区分并应用适当技术的能力。
稳健的模型验证：超越准确率，包括业务指标、偏差分析和子群体行为。
开发规范性
专业使用 Git 作为常规工作流程的一部分，而非交付时的形式。
组织良好且模块化的 Python 代码：候选人必须产出可交付的代码，而不仅仅是探索性笔记本。
熟悉实验追踪工具（如 MLflow 或类似工具）以实现实验可追溯性。
能够以结构化方式记录模型：解决什么问题，使用什么数据，存在哪些限制。
具备在团队标准下工作的经验：安全的凭证处理、数据版本控制、项目结构。
AI 判断力
负责任地使用生成式 AI 工具作为助手：具备审查和验证其输出的批判性能力。
判断何时使用代理系统或大语言模型是合适的，何时不合适。
推荐经验
搜索说明
选拔过程包括实际技术评估和对候选人过往工作的审查。
我们将更重视推理能力和判断力，而非编码速度。
我们不寻找那些只知使用工具但不了解其原理的候选人。
查看英文原文
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.
Lo que ofrecemos
- Programas de formación 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
Inetum 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:
Originally posted on Himalayas