Expert Data Scientist VOIS

Job Description

Role purpose:

The Expert Data Scientist is a core member of the department, responsible for standardizing the design and development of analytical models and insight packs. They transform raw data into actionable intelligence using predictive and descriptive statistical techniques across various data stores. They manage teams and leverage their business exposure to deliver impactful data-driven solutions. With advanced statistical and machine learning knowledge, they uncover insights and develop predictive models to solve complex business problems. They work closely with cross-functional teams, making data-driven recommendations for strategic decision-making. Through their leadership and expertise, they promote best practices in data science, enabling organizations to extract maximum value from their data assets.

Key accountabilities and decision ownership:

• Guides junior & Senior data scientists within the team and acts as a technical reference.
• Drive the establishment of best practices and standardization in data science.
• Engage with stakeholders to understand data requirements and deliver tailored solutions.
• Develop and implement analytical models and insight packs to extract actionable intelligence from raw data.
• Transform and cleanse data from various sources, ensuring data quality and accuracy.
• Provide data-driven recommendations and insights to support strategic decision-making.

Core competencies, knowledge, and experience:

• Bachelor’s degree in computer science and/or engineering or equivalent with 5+ years of relevant experience.
• Working in international, distributed teams.
• Confident and able to liaise and influence at all levels within Vodafone and/or relevant customer organizations.
• Able to communicate effectively across organizational, technical, and political boundaries, understanding the context.
• Understands business requirements, Drives improvements and process innovation.

Must have technical / professional qualifications:

• Strong statistical and mathematical background, including knowledge of probability theory and regression analysis.
• Able to mentor other team members on different skill levels.
• Can hold internal technical trainings.
• Participate and create SOPs (Standard Operating Procedures)
• Proficiency in programming and software engineering, with expertise in Python, R, SQL, and familiarity with frameworks like TensorFlow, PyTorch, and scikit-learn.
• Data manipulation and pre-processing skills, including data cleaning, feature engineering, and efficient handling of large datasets.
• Advanced machine learning techniques knowledge, such as supervised and unsupervised learning, regression, classification, and clustering algorithms.
• Data visualization and communication abilities, utilizing tools like Tableau, matplotlib, or ggplot to effectively communicate complex insights.
• Familiarity with big data technologies, including experience with Hadoop, Spark, or Apache Kafka for processing and analyzing large-scale datasets.
• Experience with deep learning and neural networks, using frameworks like TensorFlow, Keras, or PyTorch for tasks such as image recognition, natural language processing, or time series forecasting.
• Knowledge of experimental design and hypothesis testing, enabling the design of rigorous experiments and the application of statistical methodologies.
• Business acumen and domain-specific knowledge, leveraging understanding of business processes and industry expertise to translate business problems into data science solutions.
• Commitment to continuous learning and adaptability, staying updated with the latest developments in data science, machine learning, and related technologies.
• Strong analytical and problem-solving skills, with the ability to analyze complex datasets, identify patterns, and derive actionable insights.
• Academic and professional background in relevant fields, with certifications in data science and machine learning highlighting a commitment to professional development.

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