Data Analyst Intern – Robot Fleet Performance
Pubblicato il 07-10-2026 - STIGA in Castelfranco Veneto
STIGA is a leading European manufacturer and distributor of lawnmowers and power garden equipment, with over 90 years of experience in the market.
We are consistently curious, conscious of trends, inspiring, and visionary – just like our customers.
We are looking for a highly analytical and proactive Data Analyst Intern to join our Connectivity Team.
Your primary goal will be to dive into our different databases, extract and interpolate data, and measure the real-time quality and health of our entire installed base of robots.
You will be the analytical engine that helps us transition from reactive troubleshooting to proactive problem-solving. Crucially, you will apply a strong business acumen to your analysis, ensuring that every data point is viewed through the lens of improving the end-user experience and driving product success.
By providing clear, quantitative data, you will directly influence how our engineering and product teams prioritize issues, firmware upgrades, and overall performance improvements.
Key Responsibilities
• Proactive Monitoring:
Identify sudden changes in robot business analyzing firmware and App bugs, or issues related to the general system before they escalate.
• Data-Driven Prioritization: Provide clear, quantitative insights that help the team understand the scale of a problem and prioritize the most critical bugs to solve.
• Dashboarding & Iteration: Build and maintain a Daily KPI Dashboard. You will review it daily with the support of the team, iterating on the metrics until you will together finalize the perfect setup, which you will then automate.
• Ad-Hoc Deep Dives: Use the daily KPI board as a starting point to conduct deeper, ad-hoc analyses to uncover the root causes of performance drops or error spike.
Skills & Qualifications
• Education: Bachelor’s degree in a quantitative field, an engineering background is preferred (e.g., Business Analytics, Industrial Engineering, Data Science, Economics, or Statistics). We value the ab