Lenovo Latin America Spanish (LAS) seeks a proactive Junior Data Scientist with strong teamwork and analytical skills to assist the Service Operations team in analyzing operational data, identifying performance trends, and supporting decision-making processes. This role is ideal for a recent graduate or early-career professional with strong analytical skills and a passion for solving real-world problems using data and AI.
Key Responsibilities
- Collect, clean, and preprocess operational data from various sources (CRM, ticketing systems, performance dashboards).
- Conduct exploratory data analysis (EDA) to uncover patterns, anomalies, and opportunities for improvement.
- Build and maintain dashboards and reports to monitor KPIs such as service levels, repair quality, resolution times, and customer satisfaction.
- Support predictive modeling and forecasting efforts to anticipate service demand and resource needs.
- Collaborate with operations managers and analysts to translate business questions into data-driven insights.
- Document methodologies, findings, and recommendations clearly for both technical and non-technical stakeholders
- Build automation of routine reporting tasks using Python, SQL, or BI tools.
- Provide operational support by addressing daily escalations related to dashboards and automation solutions.
- Contribute to the development of AI-ready datasets by ensuring data quality, consistency, and proper labeling for supervised learning use cases.
- Support the implementation and testing of foundational AI models (e.g., classification, clustering, anomaly detection) that enhance operational efficiency and decision-making.
Required Skills
- Proficiency in Python for data analysis. (Must)
- Solid understanding of statistics and data visualization techniques.
- Experience with SQL and relational databases. (Must)
- Advanced English Level (Must)
- Familiarity with BI tools (e.g., Power BI, Tableau, Looker).
- Ability to communicate findings effectively through oral and written reports.
- Strong problem-solving mindset and attention to detail.
- Basic understanding of processes and workflows is a plus.
- Exposure to machine learning concepts and libraries (e.g., scikit-learn, TensorFlow, PyTorch) is desirable.
