Data Scientist

About e& CX Solutions

It's one of Etisalat affiliates that offers business process outsourcing services. With Our highly skilled team, cutting-edge technology, and a decade of expertise in empowering business growth, we enable you to optimize operations and enhance customer experiences. e& global services in Egypt cater excellence to your ever-changing lifestyle and ever-expanding demands – covering not only business process outsourcing, but a wide collection of services that suit your various needs. We will continue to be your trusted companion and consultant, enabling connectivity and everything beyond, accelerating the digital world and pioneering future-forward spaces.

Roles & Responsibilities:

• Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to customers.
• Use machine learning tools and statistical techniques to produce solutions to problems.
• Test data mining models to select the most appropriate ones for use on a project.
• Maintain clear and coherent communication, both verbal and written, to understand data needs and report results.
• Create clear reports that tell compelling stories about how customers or clients work with the business.
• Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods.
• Horizon scan to stay up to date with the latest technology, techniques, and methods.
• Conduct research from which you'll develop prototypes and proof of concepts.
• Look for opportunities to use insights/datasets/code/models across other functions in Etisalat.
• Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.

Main Requirements:

• 2-3 years of experience in Data Science.

• Strong Python skills (Tensorflow, Keras, Sklearn, Pandas, Numpy)

• Recommendations Engine

• Advanced SQL skills & Data Warehouse concepts

• Oracle/IBM Netezza know-how is a plus

• Probability & Statistics (A/B Testing, Probability Distributions, Summary Statistics)

• Supervised and Unsupervised Learning (Models understanding)

• Model Evaluation Metrics (Precision, Recall, F1 Score, etc.)

• Data Analysis (Outlier Detection, Dealing with Class Imbalance, Data Synthesis)

• NLP & Sentimental Analysis