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Promotional Optimization Analyst

Computrabajo
Full-time
Onsite
No experience limit
No degree limit
Cuajimalpa, CDMX, Mexico
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Description

Job Summary: GEPP is seeking a Promotional Optimization Analyst to manage massive information and analyze data for the traditional channel. Key Highlights: 1. Key role in promotional optimization 2. Handling and analysis of large volumes of data 3. Opportunity at a national leader company Description GEPP, a portfolio development company of leading brands with nationwide presence and over 46,000 employees, invites you to join its great family as: · PROMOTIONAL OPTIMIZATION ANALYST (TRADITIONAL CHANNEL) Work Location: Santa Fe, CDMX. · Talent Profile Education: Completed Bachelor's degree in Finance, Economics, Engineering, Actuarial Science, Mathematics, or related field. Experience: 1 year. EXPERIENCE/KNOWLEDGE: Handling of massive information Information sources (Nielsen, ISCAM, etc.) Office suite (Excel and PowerPoint) Management of large-scale databases RESPONSIBILITIES: Tracking key discount indicators Handling large volumes of data Consolidation of information Management of Nielsen variables Mandatory: 100% completed Bachelor's degree (in process of graduation or already graduated) Intermediate/advanced Excel Intermediate/advanced PowerPoint Intermediate English Desirable additional knowledge: Understanding of RGM (Revenue Growth Management) Knowledge of the beverage industry Programming language skills: SQL, Python, etc. · At GEPP, we value talent: We offer: - Competitive salary - Statutory and above-statutory benefits · SUCCESS STORIES BEGIN HERE! Participation in GEPP’s and/or its subsidiaries’ recruitment, evaluation, selection, and hiring processes is completely free of charge. If anyone requests monetary deposits or other compensation to participate in such processes, please ignore the request and report it immediately. GEPP assumes no responsibility in such cases.-Requirements- Minimum education: Higher education - Bachelor's degree 1 year of experience Keywords: analyst, optimization

Source:  computrabajo View original post
Mateo García
Computrabajo

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Computrabajo
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