Kate Asarar

- Data scientist for building, optimizing and deploying pricing ML model for use in production by sale and purchasing stakeholders - Achieved 10% accuracy improvement of LightGBM model by improving data quality, using regex cleanup and statistical methods. - Established way of working for an optimal collaboration between data acquisition, data science and product teams on changes and problems. - Led initiative to establish best practice and guidelines for data science in the company.

- Translated business goals into functional requirements, built software solutions and tested in MIL, SIL and vehicle. - Analyzed software feature problems to understand underlying root causes and communicated solutions to stakeholders. - Successfully led a development team of 7, achieving a company wide ASPICE level 2 rating in 4 months using Agile methods. - Organized scrum events and trainings in agile working methods and drove the interaction between team, external stakeholders and managers.

- Detailed simulation of equivalent circuits of competitor topologies. - Integration of a smart control algorithm for efficiency optimisation. - System signal analysis for simulation validation. - Analysis of different battery circuit diagrams’ influence on impedance.

5 month nanodegree program consisting of several courses and real life data projects for specialization in data science including data engineering, transforming, analytics and machine learning.

One year long participation in exchange program at the Mechanical Engineering department. Cours subjects: Machine Learning, Leadership, and Electric Vehicle Development.

Focus: Machine Learning, Electric Vehicles. Thesis: Machine learning research project for developing best fit model for predicting comfort in autonomous driving, using acceleration measurements as input and estimated passenger comfort values as Output. Performed at BMW in Munich, Germany.

Focus: Vehicle Engineering, Statistics. Thesis: Analysis of link forces on a formula student car suspension system through stress test and simulation of front suspension in ADAMS.