QUALIFICATIONS
We are looking for an 'Data Scientist' for Borusan Machine and Power Systems.
· Bachelor’s degree in Computer Science, Economics, Statistics, Mathematics or Engineering departments of leading universities. Master’s degree is a plus
· Minimum 3 years of related job experience
· Experienced in big data analytics, statistics, predictive modeling, customer behavior analytics, demand forecasting and machine learning
· Proficiency in at least one of the statistical packages, i.e. SPSS, SAS, R
· Strong knowledge of database management systems like SQL and big data frameworks
· Experience in testing hypotheses from raw data sets, drawing meaningful conclusions, and effectively communicating results
· Ability to work with cross functional teams to translate business issues into potential analytics solutions
· Knowledge and experience in SAP modules is a plus
· Basic financial analysis knowledge is required
· Good command of English
· Strong and effective communication, presentation and visualization skills
· Experience in communicating complex concepts with others coming from diverse backgrounds
· Domain knowledge in similar businesses is a plus
JOB DESCRIPTION
· Responsible for turning big data into actionable insights
· Develop and carry out projects that enhance internal user experiences, operational performance, strategic decision making through big data analytics and predictive models
· Prepare pre and post analyses on operations, campaigns and projects guiding senior management and business owners
· Integrate data analytics and modeling solutions into daily business operations and ensure effective utilization of such tools, taking account of user needs, technology and operational landscape
· Ensure maintenance and top performance of implemented analytic solutions, tools, models and implement solutions to sustain quality of data
· Identify new analytics trends and opportunities to drive the digital transformation and innovation agenda across business functions
· Be recognized as an expert, mentoring and advising colleagues on value generation through statistical techniques, algorithms and data sources
· Work in a multi-disciplined environment
