**Responsibilities**:
1. **Robot Construction**: Develop and design a software robot capable of analyzing various sources of information related to failure modes.
2. **Failure Categorization**: Implement algorithms to categorize failure modes and assess the severity of each identified failure.
3. **Monthly Profile Generation**: Develop systems to provide detailed profiles of identified failure modes, anticipating patterns and trends for the upcoming month.
4. **Data Exploration**: Utilize advanced data exploration techniques to identify relevant correlations and patterns in the collected information.
5. **Prediction Methods**: Implement advanced prediction methods to anticipate potential failures and mitigate risks.
6. **Decision Systems**: Design data-driven decision systems to facilitate informed decision-making.
7. **Temporal Data Integration**: Handle temporal data effectively, considering the evolution of failure modes over time.
8. **Recommendation Systems**: Develop recommendation systems to enhance efficiency and decision-making based on failure analyses.
9. **Neural Networks**: Implement and enhance neural networks to optimize the robot's performance in data analysis.
11. **Proficiency in R Language**: Use the R programming language effectively for statistical analysis and data modeling.
**Requirements**:
- Proven experience in software development and programming, with a special focus on AI and data analysis.
- Strong knowledge of prediction methods, decision and recommendation systems, as well as neural networks.
- Advanced skills in using the R programming language.
- Ability to design and build efficient systems capable of anticipating and categorizing failure modes.
- Strong problem-solving skills and the ability to work in a collaborative and dynamic environment.
- Knowledge: R, Python, Git Integrated Development Environment (IDE): Como RStudio (para R) o Jupyter Notebooks (para Python), TensorFlow y PyTorch, scikit-learn, caret: Paquete en R, Tableau, Power BI, Matplotlib y Seaborn, GeoPandas: Extensión de Pandas, sf, Keras,TensorFlow y PyTorch, Jenkins: Para automatizar la construcción, prueba y despliegue de software, MySQL o PostgreSQL.
- Recuerda que la elección de herramientas puede depender de los requisitos específicos del proyecto y de las preferencias del equipo de desarrollo. También es importante estar al tanto de las últimas tendencias y avances en el campo de la inteligencia artificial y el análisis de datos.
If you are passionate about software development, artificial intelligence, and ready to tackle the challenge of building an intelligent robot that will revolutionize failure mode analysis, we want to hear from you! Join our team and be part of the innovation in the field of technology.
