THE PROJECT

Our client is a highly relevant innovation company that helps people connect, improve businesses’ growth, and build the future of social technology worldwide. They have built a team of over 10,000 people across all continents, and their mission is to bring the world closer together with the use of their services and technologies.

THE ROLE

As a Research Data Scientist, you will be responsible for doing the strategic analysis to allow the continuous growth and improvement of the organization. The team is made of computing experts that use all kinds of quantitative disciplines to measure and optimize the cost and efficiency of the global telecommunication systems, to offer the best possible experience to their global audience.

DUTIES AND RESPONSIBILITIES

• You will work in a functional way to define error identification, data compiling, and create analytical models.
• Create statistically rigorous solutions for the web, mobile, and data infrastructure issues by leveraging and developing statistical methodologies.
• Develop systems that will normalize and clean datasets, develop data pipelines from different sources, and create a structure for non-structured data
• Create and maintain optimization models based on data, forecasting algorithms, make recommendations and communicate them
• Leverage tools such as R, Python, Hadoop, SQL, etc. to improve the efficiency of analysis.

THE REQUIREMENTS

• Over 1 year of industry experience or postgraduate in solving analytical problems and creating quantitative, statistical or machine learning models
• Degree in the quantitative field (Computing Science, Statistics, Mathematics, Engineering, etc.)
• Experience with Machine Learning, Statistics, and other analytical tools and processes
• Experience with at least one programming language (Python, R, Java, C++, etc.) and in writing SQL queries
• Experience carrying out data extraction, cleansing, analysis, and presentations for medium-large datasets
• Experience with statistical methodologies for forecasting, time series, hypothesis tests, classification, or regression analysis.
• Experience with scientific computing analysis packages like Pandas, NumPy, SciPy, Scikit-learn, etc.
• Experience with visualization libraries such as Matplotlib, Pyplot or ggplot2; and with machine learning packages such as PyTorch, Caffe2, TensorFlow or Keras

VALUABLE SKILLS

• Higher Degree (Master’s or PhD) in the quantitative field
• Advanced knowledge of algorithmic complexity
• Experience working with distributed computing tools like Hadoop, Hive, Spark, etc.