Senior Data Analyst, Data Visualization Expert

Summary

Join Xebia, a leading digital solutions provider, as a Data Analyst to conduct analysis, support business stakeholders, and create dashboards. Requirements include 3+ years of experience analyzing complex data, applied data visualization skills, and coding skills in R or Python.

Requirements

• Ability to start immediately
• Openness to work daily between till 19.00 pm CET
• 3+ years of professional experience analyzing complex data, drawing conclusions, and making recommendations
• 3+ years of applied data visualization experience - Tableau and PowerBI as main visualization tools
• 3+ years of experience in extracting & manipulating large data sets from various relational databases using SQL (Amazon Redshift, Oracle, Google BigQuery)
• Coding skills in at least one statistical or programming language (R or Python preferred) to import, summarize, and analyze data
• Hands-on experience working with big data, ideally clickstream data, in big data volume sectors such as ecommerce, gaming, social network etc. ecommerce platforms are a plus
• Ability to translate and present complex analysis in executive summaries - clear and effective written and verbal communication and strong interpersonal skills
• Strong problem solving skills
• Bachelor's in economics, Statistics, Data, Data Science and Engineering (Masters is a plus) or equivalent experience
• Good verbal and written communication skills in English
• Work from the European Union region and a work permit are required

Responsibilities

• Conducting analysis to estimate expected value of data products
• Measuring and tracking benefits created by data products, and converting them to financial or key KPI metrics
• Supporting business stakeholders and regional data analysts by understanding their needs and providing guidance and support
• Creating dashboards to track adoption and business impact of features and data product launched
• Diving deep into complex business problems and providing insights on digital features and AI model performance through hypothesis testing; partnering with cross functional teams on implementation
• Partnering with data science, data analytics and product managers on planning, goal setting and prioritization
• Partnering with data and engineering teams to improve instrumentation and product/models health reporting, pre/post analysis and business debugging
• Partnering with A/B test team to design experiments and post-hoc analysis of results
• Bringing data to life through storytelling in a clear and meaningful way to audiences with mixed levels of technical expertise and informing key strategic decisions
• Promoting a culture of data driven technical excellence, ownership, collaboration