Position: Data Scientist (IRC89677)
Company: GlobalLogic
Category: Data Science
Experience required: 3y
English level: upper
Published: 2020-03-01T00:00:00+02:00

- 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 modelling, multivariate analysis, machine learning and statistical modelling, and preferred additional knowledge of time series modelling 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 team work, 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.

Job Responsibilities
- Research statistical models for monitoring and predictive maintainence 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 modelling to both a technical and non-technical audience
- Learning new engineering practices, technologies and continuously improving our Agile practices

Department/Project Description
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.