Data Analyst

Requirements

• We are looking for professionals with these required skills to achieve our goals:
• Degree in statistics, mathematics, data science, computer science, economics, life sciences or a related field
• +4 years of experience working with data, reporting or analytics
• Impressive hands-on career to date in an advanced and innovative enterprise environment in the data and analytics space
• Extensive hands-on experience as a Business and/or Technical Data Analyst
• Proven history delivering complex projects in the Analytics space
• Extensive experience with at least 1 data modeling and visualization tool, preferably Power BI
• High proficiency in SQL querying (preferably T-SQL)
• Proven experience in leveraging Cloud data platforms such as Azure Data Lake, Synapse (SQL Datawarehouse) and Azure Databricks
• Please note the following skills are not necessary, just preferred, if you do not have them, please still apply:
• Experience in working in agile environment, including writing epics, features, stories and creating workflow/process flow diagrams
• Experience in communicating requirements and raising incidents in an enterprise ITSM (ServiceNow), working with offshore support teams
• Strong understanding and practical experience in predictive and statistical modeling, machine learning algorithms and clustering techniques
• Strong background in technology, analysis and critical evaluation of information gathered from multiple sources
• Experience/knowledge of ML modeling and ML Engineering
• Innovative mindset/experience in a Healthcare/Pharma company
• Excellent conceptual and analytical skills
• Problem-solving, teamwork and commercial awareness
• Organizational and time management skills and autonomy in self-organizing activities
• Accountability for results and value driven initiatives
• Excellent written and verbal communication skills, both in Italian and English.
• Technical knowledge:
• Data engineering and coding experience in the relevant languages commonly used in data analysis: Python, SQL, Scala, etc.
• Advance Experience with the Azure data stack: Databricks, Data

Benefits

Additional Information

We create a place where people can grow, be their best, be safe, and feel welcome, valued, and included. We offer a competitive salary, an annual bonus based on company performance, healthcare and wellbeing programmes, pension plan membership, and shares and savings programme. We embrace modern work practises; our Performance with Choice programme offers a hybrid working model, empowering you to find the optimal balance between remote and in-office work. Discover more about our company wide benefits and life at GSK on our webpage Life at GSK | GSK Scope: As a Technical Data Analyst, you will be responsible for: Understanding in depth and supporting rollout of reporting solutions developed by Commercial and Medical Data Analytics and Insights Global teams Communicating local data and analytics requirements to Global teams, partnering with them to develop global solutions, including pilots Implementing local reporting solutions leveraging Global data frameworks, based on business requirements and priorities. In this role you will: Understand in detail GSK Global Data Framework, including various semantic layers and all relevant data/analytics definitions in the commercial and medical space, and share global and regional KPIs definitions with local business teams as needed Implement local Commercial and Medical Customer & Operational Analytics reporting solutions to enhance analytics and insights capabilities without introducing redundancy with global solutions Develop user stories, technical requirements/specifications, data architectures and data models (e.g., data mapping, logical data model) for Commercial and Medical Customer & Operational related projects, mainly focused on implementing dashboards and reporting solutions, to be shared with internal and external global or local engineering teams Develop test scenarios and test cases along with acceptance criteria and identify and drive opportunities to reuse existing data models in local environment Elicit business requirements from key stakeholders via interviews, document analysis, requirements workshops, competitive product analysis, task and workflow analysis, and effectively manage technical and non-technical communications with engineering team members, internal and external partners, stakeholders, program staff and end users Monitor and document post-implementation problems and revision requests to ensure they meet end-user needs Understand when to highlight risks or issues and clearly communicate them to management teams and stakeholders. Key Stakeholders (Internal and External): Commercial/Business Excellence Business Partners and Business Analysts Marketing and Medical Teams Global Commercial and Medical Data, Analytics and Insight Teams Marketing Operation teams Third Party Data, Analytics and Digital Services providers. Why You?

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