Lead Data Scientist - Expert Analytics

at  Ericsson

ABOUT THIS OPPORTUNITY:

We are looking for a Lead Data Scientist to develop and maintain components that deliver Telecom Insights on top of the Ericsson Expert Analytics (EEA) portfolio.
EEA is a powerful tool offered to telecom operators producing customer insights in real time. Backed by specialized metrics for predicting customer happiness, EEA provides a deep understanding of customer behaviour that allows service providers to proactively improve the experience, satisfaction, average revenue per user and Net Promoter Score.
We work in small teams that use Agile methodology to keep up in a fast-paced business environment, we closely cooperate with Ericsson Research, customer units and service delivery organizations. Our product platform combines Big Data methodologies and solutions with our outstanding complex event processing to build a state-of-the-art stream-based analytics and decision management system.

THE SKILLS YOU BRING:

• 4+ years’ experience in data science (both industry and academy are welcome)
• 6+ years’ of work experience
• Demonstrated experience with Python
• Familiarity with Machine Learning techniques and libraries
• Experience in model creation and prototyping
• A good understanding of software development methodologies
• Appropriate soft skills to handle different human behaviours
• Excellent communication and presentation skills
• Solid mathematical, and statistical background with strong and creative problem-solving skills.

• Be a technical lead of the Data Scientist team
• You will shape the team development processes
• Develop Telecom Insights using AI /ML with support from domain specialists and a team of data engineers
• Work on use cases using the newest tools and methodologies in the AI/ML area
• Collaborate with various teams, such as DevOps teams, BI teams and business stakeholders
• Develop and validate Proof of Concepts, support use case industrialization and testing, and engage in AWS ML pipeline and use case creation
• Build organizational DevOps/MLOps expertise