About Our Project:
Within the digital transformation strategy, Kyivstar is constantly improving it’s data management platform (DMP) based on Big Data technologies, Hadoop ecosystem and cloud computing. One of the streams is development and implementation of data-products for internal and external customers using Big Data and Machine Learning technologies. So we are looking for a Senior Data Scientist: expert in the field of machine learning and analytics with practical skills in ML Pipelines development using Open Source tools.
• A unique experience of working for the largest and most beloved mobile operator in Ukraine
• Real opportunity to ship digital products to millions of customers;
• A competitive salary;
• Annual bonus;
• Paid sick leave and vacation;
• Financial aid in different life situations;
• Possibility to work remotely;
• Flexible working hours;
• Medical and life insurance;
• Great possibilities for professional development and career growth;
• Friendly & Collaborative Environment;
• Microsoft Azure certification.
• 3+ year experience with Python (statistical and ML packages), advanced knowledge of SQL;
• Understanding theoretical concepts of statistics/probability, machine learning (not just training models);
• Experience in implementing data science to achieve commercial goals;
• Excellent business understanding, problem-solving abilities, and organizational skills;
• Ability to build meaningful visualizations of results.
Will be a plus:
• Experience with Data Sciences within Telecom;
• Knowledge and hands-on experience with one or more of the following: Spark/Pytorch/TensorFlow/Scala;
• Experience with building end-to-end reusable pipelines from data acquisition to model output delivery;
• Kaggle challenges.
• Preprocess data for analysis and conduct Exploratory Data Analysis (EDA), generate insights from data;
• Develop pipelines with ML/AI algorithms;
• Contribute to ML ecosystem development;
• Communicate and coordinate with Data Engineers;
• Explain modelling approaches and discuss results/business impacts with non-technical staff.