Analyze historical data to identify trends, anomalies, and performance drivers
Translate data-driven insights into actionable recommendations to support diagnostics and business decisions for product development
Design and build practical algorithms based on data thresholds and business heuristics to support decision-making
Provide dedicated data-driven support for Engineering Development, including punctual analysis and defining data requirements
Analyze customer ticket data to identify patterns and opportunities for product improvement
Collaborate closely with software engineers, product managers, and other stakeholders to embed data insights into the development lifecycle
Create and present compelling reports and data stories to communicate findings effectively to both technical and non-technical audience
Work closely with software engineers, product managers, and data scientists, data engineers and hardware engineers

Highly proficient in Python and its data analysis libraries, including Pandas, Matplotlib, and PySpark. Basic proficiency in Bash/Linux commands.
Possesses a strong foundation in descriptive analytics, with demonstrated critical thinking and problem-solving capabilities
Capable of working effectively with both structured and unstructured data.
Writes clean, maintainable code and consistently applies version control (Git) as a standard practice within the workflow.
Capable of working effectively with both structured and unstructured data.
Knowledge of dashboard visualization tools, such as Streamlit, would be considered an advantage.
Demonstrates strong data storytelling abilities and delivers clear, compelling reports and presentations tailored to diverse audiences.