Commercial Analyst

Position Summary

We are seeking a highly skilled Commercial Data Science & Analytics Specialist to strengthen our end-to-end analytics capability across the FMCG commercial landscape. This role will design and operationalize advanced analytics models, enhance data systems and governance, and transform complex datasets into actionable insights that accelerate sales growth, execution excellence, and route-to-market performance.

The ideal candidate possesses strong data science capability, deep understanding of FMCG commercial operations, and the ability to translate data into decisions for cross-functional stakeholders.

Key Responsibilities

1. Advanced Analytics & Data Science

• Build predictive models supporting commercial decisions such as outlet potential, assortment optimization, pricing insights, demand forecasting, territory clustering, or sales opportunity identification.
• Apply machine learning techniques (classification, regression, segmentation, clustering, feature engineering).
• Develop experimentation frameworks (A/B testing, uplift modeling) to measure impact of commercial initiatives.
• Maintain versioned model libraries, ensuring scalability, documentation, and reproducibility.

2. FMCG Commercial Insights & Business Analytics

• Analyze large and diverse datasets (sales, distribution, retail execution, route-to-market, consumer/shopper behavior).
• Generate insights that support sales growth, merchandising strategy, market development, and channel performance.
• Distill commercial opportunities into clear “so-what” recommendations for Sales, Trade Marketing, and Commercial Excellence teams.

3. Data Operations & Systems Management

• Manage routine data operations including data ingestion, validation, anomaly detection, and dashboard refresh cycles.
• Ensure accuracy and consistency across data sources (sell-in, sell-out, distribution, retail store universe, master data).
• Serve as a Level-2 (L2) focal point for commercial data issues: root-cause analysis, triage, documentation, and process improvement.
• Maintain data governance assets—SOPs, runbooks, data dictionaries, metric definitions, and change logs.

4. Data Quality & Governance

• Lead data quality improvement across key datasets: product hierarchy, customer master, sales structure, route-to-market mapping, retail outlets, execution KPIs.
• Implement best-practice governance frameworks including data integrity checks, access controls, and audit compliance.
• Partner with IT/Data Engineering to enhance data pipelines and system stability.

5. Cross-Functional Collaboration & Stakeholder Engagement

• Work closely with Sales, Trade Marketing, Route-to-Market, Supply Chain, and Finance to understand business needs and translate them into analytical solutions.
• Present insights through storytelling, visual analytics, and well-structured business recommendations.
• Coach commercial users on model interpretation, analytics literacy, and data-driven ways of working.

Required Qualifications

Technical Competencies

• Strong proficiency in Python (pandas, numpy, scikit-learn), SQL, and data visualization tools (Power BI preferred).
• Experience in designing and implementing predictive models and automated analytics workflows.
• Familiarity with FMCG datasets such as sales performance, distribution coverage, channel execution, and retail store data.
• Understanding of data architecture concepts, ETL processes, and analytical model deployment.

Business & Functional Competencies

• Experience in FMCG, retail, consumer goods, or commercial analytics is strongly preferred.
• Solid understanding of FMCG commercial levers: distribution, assortment, pricing, promotion, sales execution, and route-to-market.
• Ability to manage large datasets with multiple hierarchies and geographical layers.
• Strong structured problem-solving skills with a “hypothesis-driven” approach.

Soft Skills

• Excellent communication and stakeholder-management skills.
• Ability to simplify complex analytics into actionable commercial insights.
• High ownership, attention to detail, and continuous improvement mindset.
• Comfortable working in a fast-paced environment with cross-functional teams.

Preferred Qualifications

• Experience deploying machine learning models in production environments (e.g., Azure ML, Databricks).
• Knowledge of data governance best practices in FMCG organizations.
• Competence in geospatial analytics (basic level acceptable).
• Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Economics, or related fields.

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