Data Scientist (IRC89678)

GlobalLogic Ukraine is inviting a Data Scientist to join its Kyiv office.

Global Logic is looking for people who have the ability to perform high-quality statistical analyses in order to develop data-driven solutions to our customers' business and technical challenges, as well as the ability to clearly and effectively present findings to the customer.

Our client is a group of petrochemical and energy companies, which helps to meet the world's growing demand for energy in economically, environmentally and socially responsible ways.

We offer:

• Interesting and challenging work in a large and dynamically developing company

• Exciting projects involving the newest technologies

• Professional development opportunities

• Excellent compensation and benefits package, performance bonus program

• Modern and comfortable office facilities

Requirements

• PhD, MSc or equivalent in Statistics, Mathematics or similar discipline with 3 years experience on data science/statistics projects.
• Passion for and expertise in practicing statistical consulting to solve real-world problems.
• Proficiency in Python, ideally with experience of pandas and scikit-learn libraries.
• Experience in general linear modeling, multivariate analysis, machine learning and statistical modeling, and preferred additional knowledge of time series modeling and signal processing.
• Awareness of issues in statistics and dependence on data quality.
• Practical common-sense approach to problem-solving and attention to detail.
• Strong interpersonal skills and enthusiasm for teamwork, as well as the ability to work independently.
• High standards of code quality, making use of version control tools.
• Knowledge of Databricks and SPARK platforms.

Responsibilities

• Research statistical models for monitoring and predictive maintenance using the latest methods
• Work closely consulting with the customer and the Product Owner day-to-day to improve upon our existing algorithms
• Work in a highly collaborative and friendly Agile environment
• Documenting and explaining the results of analysis or modeling to both a technical and non-technical audience
• Learning new engineering practices, technologies and continuously improving our Agile practices