Curtis Wood (PhD) has three years of experience in predictive train maintenance and almost 20 years of experience in data analytics (previously working in atmospheric physics). Currently, Curtis works as a Data Scientist on predictive maintenance at VR FleetCare. In his work, he focuses on lifecycle optimization and lengthening for wheelsets. Most of his time is spent wrangling and analysing data from IOT sources on an in-house Data Platform using AWS, SQL, Python among other tools.