About the Role
We are looking for an experienced data scientist with a strong background in algorithms, data management, scenario modelling, optimization and machine learning.
In this role, you will help design, implement, and deploy AI-driven models that improve accuracy, efficiency, and scalability for the Revenue Growth Management (RGM) capability driving pricing, trade investment, and brand investment decisions.
This role sits at the intersection of data engineering, machine learning, and commercial application. Your mandate is to take identified algorithms and make them into a production-grade, self-sustaining system embedded in SGS's day-to-day RGM workflows. You are the bridge between the algorithm as designed and the tool as used.
Key Responsibilities
1. Tool Operations & Management
Connect and automate data flows from SAP/ERP, Salesforce, Anaplan and other internal tools as inputs to the RGM tools
Own data quality, freshness, and schema governance across all model inputs: sell-in, sell-out, MRP, cost curves, TI/BI spends, promo calendar, outlet-level data across all markets
Build data validation, reconciliation, and lineage frameworks to ensure model inputs are auditable and trustworthy
Manage data refresh cadences across structured sources (SAP, Salesforce, IWSR, Anaplan, etc.)
Proactively identify and resolve data gaps, quality failures, and schema drift before they surface in model outputs
2. Algorithm Management
Monitor live model accuracy against thresholds, distinct by model type (e.g., pricing elasticity vs. TI/BI ROI decomposition), and recommend / perform model refreshes basis updated data-sets & inputs
Retrain and redeploy pricing elasticity and TI/BI optimizer models at the required granularity and cadence
Maintain version control and reproducibility of model iterations; document rationale for retraining decisions
3. Current Data / Source Ingestion
Evaluate and integrate new structured and unstructured data sources (e.g., alternative/enrichment da
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