Analyst Perfect Store with Power BI

This role involves generating detailed reports and visualizations, analyzing data to identify sales performance drivers, and developing predictive models for Perfect Store strategies. Key responsibilities include performing linear regression analysis, collecting and organizing large datasets, and maintaining regression models. The analyst will collaborate with Perfect Store and Category managers to tailor analyses to specific business needs.

Must Have

• Generate detailed reports and visualizations
• Analyze and interpret data to identify key drivers of sales performance and store efficiency
• Develop predictive models to forecast outcomes and optimize Perfect Store strategies
• Perform linear regression analysis for the Perfect Store initiative
• Collect, clean, and organize large datasets from multiple sources
• Maintain and update regression models regularly
• Work closely with Perfect Store managers, Category managers teams, and other stakeholders
• Bachelor’s degree in Statistics, Data Science, Mathematics, Economics, or a related field
• Min. 2 years of experience in data analysis
• Proven experience with data visualization tools such as Power BI and Microsoft Excel
• Excellent attention to detail and accuracy in data analysis and reporting
• Fluency in English

Good to Have

• Experience in a shared service center or multinational environment
• Knowledge of statistical software
• Knowledge of programming languages such as R, Python

Perks & Benefits

• Flexible work scheme with flexible hours, hybrid work model and up to 30 days per year for remote work abroad
• Diverse national and international growth opportunities
• Globally applicable well-being standards, including health and preventive care programs
• Gender-neutral parental leave for a minimum of 8 weeks
• Employee Share Plan with voluntary investment and Henkel matching shares
• 13th salary, incentives
• Cafeteria benefit system, Multisport card
• Henkel shop and Henkel nursery school
• Extra paid days off for special life situations, referral and relocation bonus

Your role

• Generate detailed reports and visualizations that present analytical findings and insights
• Analyze and interpret data to identify key drivers of sales performance and store efficiency
• Develop predictive models to forecast outcomes and optimize Perfect Store strategies
• Perform linear regression analysis for the Perfect Store initiative across various product categories and countries
• Collect, clean, and organize large datasets from multiple sources to ensure data accuracy and reliability
• Maintain and update regression models regularly to reflect changing market conditions and business needs
• Work closely with Perfect Store managers, Category managers teams, and other stakeholders to understand specific needs and tailor analyses accordingly

Your skills

• Bachelor’s degree in Statistics, Data Science, Mathematics, Economics, or a related field
• Min. 2 years of experience in data analysis
• Proven experience with data visualization tools such as Power BI and Microsoft Excel
• Experience in a shared service center or multinational environment is a plus
• Knowledge of statistical software is an advantage
• Knowledge of programming languages such as R, Python is a plus
• Excellent attention to detail and accuracy in data analysis and reporting
• Fluency in English
• Team focused with ability to work across virtual teams

PLEASE SEND YOUR CV IN ENGLISH

Benefits we offer

• Flexible work scheme with flexible hours, hybrid work model and up to 30 days per year for remote work abroad
• Diverse national and international growth opportunities
• Globally applicable well-being standards, including health and preventive care programs
• Gender-neutral parental leave for a minimum of 8 weeks
• Employee Share Plan with voluntary investment and Henkel matching shares
• 13th salary, incentives
• Cafeteria benefit system, Multisport card
• Henkel shop and Henkel nursery school
• Extra paid days off for special life situations, referral and relocation bonus