Data Scientist

BRİSA BRİDGESTONE SABANCI LASTİK SANAYİ VE TİCARET ANONİM ŞİRKETİ

We are looking for a Data Scientist who will help us create value from vast amounts of data we collect, and help us make smarter decisions to deliver even better products. Your primary focus will be in applying machine learning models, doing statistical analyses, and building high quality prediction systems integrated with our products, commercial and manufacturing processes.

Educational / Experiential  Background :

• BS/MS degree in related fields such as engineering, computer science, statistics, mathematics,  economics, ops research or related technical discipline
• Excellent understanding of database systems, statistical analysis, Machine Learning and Deep Learning techniques and algorithms / methods 
• Having query language experience (SQL etc.)
• Experience with modelling techniques and advanced applied skills such as significance testing, GLM/Regression, Random Forest, Boosting, Trees and using tools like Python, Spark
• Fluency in English
• Strong analytical and problem-solving skills
• Background in programming in Phyton, R, C, C++ beneficial
• Professional attitude and service orientation; superb team player
• Good communication skills along with strong desire to work in cross-functional teams
• Able to build a sense of trust and rapport that creates a comfortable & effective workplace

Job Description

• Identifying opportunities, defining problems, and establishing predictive analytics models, and delivering optimal solutions
• Closely working with other business functions such as marketing, sales and operations in order to perform advanced analytics based on functional and business requirements
• Collaborating with Data Operations and Manufacturing IT to identify and acquire access to necessary data
• Extending Brisa’s corporate data with third party sources of information when needed
• Processing, cleansing, and verifying the integrity of data used for analysis
• Selecting features, building and optimizing classifiers using machine learning models
• Creating automated anomaly detection systems and constant tracking of its performance
• Answering industry/business questions using data
  
• Leveraging large volumes of data (internal and/or external)
  
• Preparing data for use in the predictive modeling
• Creating accurate models and discovering patterns
• Finding insights to answer business questions and/or improve business