Marina Selivanova
About Marina Selivanova
Marina Selivanova is a Data Analyst at Xeneta in Oslo, Norway, with a background in financial analysis and business analytics. She holds a Master of Science in Business Analytics from BI Norwegian Business School and has experience in data engineering and mentoring interns.
Work at Xeneta
Marina Selivanova has been employed at Xeneta as a Data Analyst since 2022. In this role, she has contributed to data analysis projects and improved data pipeline efficiency by adjusting AWS Lambda, Docker, and Terraform. Prior to her current position, she worked as a Junior Data Analyst at Xeneta from 2021 to 2022, where she gained foundational experience in data analytics within the company.
Education and Expertise
Marina Selivanova holds a Master of Science in Business Analytics from BI Norwegian Business School, where she studied from 2021 to 2023. She also participated in an exchange semester at Hochschule Hof, University of Applied Sciences, focusing on International Management in 2018. Her undergraduate education includes a Bachelor's degree from Plekhanov Russian University of Economics, where she studied from 2015 to 2019. Additionally, she engaged in QTEM, specializing in Business Intelligence and Big Data, further enhancing her analytical skills.
Background
Before joining Xeneta, Marina Selivanova worked in various analytical and consulting roles. She served as a Financial Analyst at ООО Алькор и Ко (Л'Этуаль) from 2019 to 2021 and as a Consultant at KPMG Russia for six months in 2019. Her early career included a position as a Sales Consultant at EF English First from 2018 to 2019 and a role as a Pool Attendant during a Work & Travel program in the United States in 2017.
Achievements
Marina Selivanova has engineered a predictive model using XGBoost and K-Means clustering techniques in AWS Sagemaker, achieving a 98% classification accuracy rate. She also formulated a coding framework that reduced coding time by 30% and pull request review durations by 25%. Additionally, she mentored a data automation intern, leading to their employment based on exceptional performance.