Data Scientist - Telecom

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Git, Azure, AWS, Apache Hadoop, Google BigQuery, Keras software library, MATLAB Programming, NumPy, Pandas, PyTorch, SciPy, TensorFlow, Theano, Docker, Google Cloud Platform (GCP), Scikit-learn, Junit, SPARK Programming, GAMS, Gurobipy

Job Description

As a Data Scientist you will be part of our highly engaged and collaborative Analytics & Platform Solutions team and tasked with exciting projects across all industries.

Combining strategic and business insights with the latest analytical techniques, we help our clients to solve critical problems in a data-driven way.

Your task is to develop innovative data-based products and quantitative-analytical solutions for our clients.

Additionally, you will collaborate with our data engineering team to implement these solutions to derive the maximum value from the data.

On a day-to-day basis, you will work with state-of-the-art quantitative methods, use a variety of modeling techniques, and apply a client-oriented approach.

During projects, you will support the business side of C-level strategy consulting on various topics that have a quantitative, data-oriented, and technical focus.

Qualifications

You have a M.Sc. or Bachelors in a quantitative field (Information Systems, Computer Science, Physics, Mathematics, etc.) and demonstrated industry experience in data science in telecom domain (minimum 2 years and maximum 5 years of experience)

Core Technical Requirements:

• Strategic and entrepreneurial thinking, strong team player
• Has relevant experience in Telecom sector and has implented use cases specific to telecom - Network optimization, Customer personalization, Retention etc. 
• Advanced practical and methodological understanding in econometrics / statistics (machine learning or nonparametric statistics); knowledge in the field of causal inference is beneficial.
• Proficient skill in at least two common programming languages (R or Python, as well as C/C++, GAMS, Stata, MATLAB or equivalent)
• Expertise in building, applying and maintaining impactful Machine Learning models using popular ML packages (e.g. NumPy, pandas, scikit-learn, caret, mlr, Spark ML, Keras, TensorFlow,PyTorch, Scipy, Theano, gurobipy)
• Experience with Big Data infrastructure and cloud-based analytics tools is an advantage (e.g., AWS, Azure, Google Cloud Platform, Docker, Spark, Hadoop, AMLS, BigQuery)
• Good command of software engineering workflows is beneficial (Git, Unit tests, Code Reviews etc.)