Data Scientist

Employment Type:
Full Time, Permanent
IT Jobs
Job Role:
Big Data Jobs, Business Analysis Jobs
Mobile Jobs
Job Ref:

If you love to work with a large and varied datasets, develop predictive models and share insights within a community of colleagues across Advanced Analytics, probably the challenge we have is the right for you!

We are looking for you to:

Be a Scientist - test hypotheses, discover hidden relationships and provide actionable insights to Vodafone’s businesses
Be part of a Global Community - design, develop & test analytic models to industrialise insight into operations to solve real business problems and enable an Advanced Analytics capability across Vodafone globally
Be in touch with different professionals - use datavisualisation to engage audience in a compelling way, enabling effective storytelling
Be Customer focused & Technology driven - delivers key packages of work, on time and in a collaborative manner to meet the needs of business customers in a complex and fast-paced environment
Be an inspiring colleague - contribute to the development of self and others across the global analytics community, ensuring Advanced Analytics is always evolving and at the cutting edge

To better succeed in this challenge previous experience (professional and/or academic) in Big Data analytics& deployment of models and algorithms to solve real-world problems (with deep statistical modelling expertise) is very important.

Technically expertise and will to be constantly learning will be crucial to keep on delivering and growing in this area:

Expertise in data manipulation: use of structured data tools (e.g., SQL), & unstructured (preferred) data tools and platforms (e.g., Hadoop, Spark, NoSQL, Hive, Kafka)
Proficient in Machine Learning and Statistical libraries (e.g. R, Python SciPy & Scikit-learn, NLTK, MLlib) with a strong statistical modelling expertise
Proficiency in at least one relevant programming language: Python, R, Scala, MapReduce, Mahout, Java, C++Expertise in major statistical modelling software packages (e.g., R, SAS) and multivariate techniques (e.g. k-means segmentation, multiple regression, factor analysis, time-series forecasting)
Familiarity with visualisation tools (e.g. Tableau, CartoDB, D3)

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