Senior Data Scientist at Vodafone Global Enterprise

Your responsibilities will include

• Create Machine Learning and AI products that provide actionable business insight to Vodafone and its stakeholders.
• Developing predictive models with large and varied datasets, working with a community of colleagues across Advanced Analytics, technology, and data and customer functions
• Contributing to the wider community to enable Machine Learning and AI capability across Vodafone globally.
• Development of machine learning models for various areas of the business on the Big Data Platform
• Development of prototype code in e.g. PySpark for automated training and scoring of the machine learning models
• Machine Learning Model performance tracking and reporting using e.g. Qlik
• Uses data visualisation to engage audience in a compelling way, enabling effective storytelling
• Work with lead data scientist to deliver key packages of work to meet the needs of business customers
• Works in partnership with Big Data Engineering for data ingestion to support use cases
• Works in partnership with Big Data Production Data Engineering for model automation and productionising 
• Deploy models in Containerized Environments using tools such as Docker and Kubernetees , allow for on-prem and on Cloud Deployment.
• Developing insightful dashboard to enable faster decision making
• Identifying new data sources and evaluate emerging technologies for data discovery usage.
• Mentoring young data scientists in the team
• Presenting the results to  technical and non- technical audiences

The ideal candidate for this role will have

Technical / Professional Qualifications:

• Bachelor’s or Master’s Degree in quantitative fields like Mathematics, Statistics, Economics, Computer Science Engineering, Artificial Intelligence or related fields (essential)
• A minimum of 5 years relevant experience in Data Science

Core competencies, knowledge, and experience:

• Experiencing working with and managing a team of junior data scientists.
• Experience in data manipulation: use of structured data tools (e.g., SQL), and unstructured data platforms (e.g., Hadoop, Spark, NoSQL)
• Proficiency in at least one relevant programming language: Python, R
• Experience in major machine learning modelling libraries (e.g., H2O, scikit-learn, PyTorch, Tensorflow) and techniques (e.g. random forest, gradient boosting, k-means segmentation, multiple regression, factor analysis, time-series forecasting)
• Exposure to cloud native deployment of models and working with Containerised technologies such as Docker and Kurbernetees.
• Knowledge of MLOPS concepts and deployment of models through batch and real-time architectures. Use of Technology such as (MLFLOW and Apache Airflow)
• Familiarity with visualisation tools (e.g. Tableau, Qlik, D3, Apache Superset, Plotly)
• Exposure/interest in machine learning 
• Professional and/or academic experience in Big Data analytics & deployment of models and algorithms to solve real-world problems (with deep statistical and  machine learning modelling expertise)
• Experience manipulating and analysing large and complex datasets /
• Experience in visualisation, creating graphical static and interactive displays of data that clearly communicate insight (preferred)
• Good interpersonal communication and presentation skills
• Ability to work in a fast-paced environment 
• Analytical and expansive thinking with a strong desire to deliver and develop