Data Analyst

Job Description We are looking for an exceptional Data Analyst to join the Coches.Net, Motos.Net and Milanuncios.com team. You will work as a member of multidisciplinary team, collaborating closely with Product owners, cluster teams, data engineers and management team stake-holders (Marketing, Sales, Product, BizDev and other Operations teams). Your main mission is to define and execute analyses of several business context to identify trends, opportunities and problems to improve the offering and services we offer. Job Requirements 3+ years of experience in eCommerce, an online company. Advanced knowledge of SQL and relational databases Advanced knowledge of Python and/or R for data analysis (e.g. pandas, numpy, matplotlib), and advanced analytics (e.g. scikit-learn) Advanced user of at least one visualisation tool such as Tableau and/or QlikSense. Analytical skills: know how to interpret data, to extract relevant insights and be able to translate business questions into analytical needs Nice-to-have Experience working with sales, operations, and strategy teams. Good knowledge of basic machine learning algorithms (e.g. clustering, classification, regressions) Nice To Have Experience working with sales, operations, and strategy teams. Good knowledge of basic machine learning algorithms (e.g. clustering, classification, regressions) Some experience with big data technology (e.g. databricks, datalakes) Some knowledge of data engineering tools (e.g. Airflow, DBT) Job Responsibilities Identify, analyse and interpret KPIs and Health Metrics related to our users and customers, creating dashboard and visualisation to monitor them. Identifying trends, risks and opportunities to provide insights that can make recommendations on how to improve the different initiatives of the vertical. Create powerful Insights using Advanced analytics to improve the knowledge of our users and customers. Actively participate in the discovery, and development of advanced analytical models (such as clustering, and predictive models), into their implementation in business use cases, such as customer segmentation, and propensity models for upselling, downselling, and churn prediction.