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

- 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

- 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

- New Data / Source Ingestion

Evaluate and integrate new structured and unstructured data sources (e.g., alternative/enrichment data such as outlet review platforms, delivery-app footprint proxies, search-interest signals)

Assess incremental predictive value of a new source before onboarding — not integration for its own sake

Update and retrain algorithms to incorporate new sources without disrupting existing production outputs

- Governance, Guardrails & Compliance

Implement business and portfolio constraints (margin floors, brand positioning rules, excise/regulatory limits handed down by Legal/Regulatory) as configurable guardrails within the live tool

Enforce data-use segregation rules — e.g., competitor-benchmarking-only datasets (such as third-party market data) must never enter internal SGS performance or recommendation models

Maintain auditability of model logic and recommendation rationale for internal and regulatory review

Education

Master's or Ph.D.



in Computer Science, Data Science, Applied Mathematics, Engineering, or a related quantitative field

Experience

Required Qualifications

- 5+ years of hands-on experience in machine learning, optimization, and applied statistical modelling
- Expertise in Python and related frameworks: scikit-learn, XGBoost/gradient boosting, and Bayesian modelling libraries (e.g., PyMC, Stan)
- Solid SQL skills; comfortable querying and reconciling large, high-granularity datasets (state × brand × SKU × channel × outlet level)
- Proficiency in optimization techniques: linear/non-linear programming, heuristics, or constraint-based modelling (e.g., LP-based budget optimizers)
- Experience building hierarchical/Bayesian models for elasticity or demand estimation, and gradient boosting models for attribution/ROI decomposition
- Experience deploying and maintaining ML models on cloud platforms, ideally GCP (Vertex AI, BigQuery); AWS or Azure also acceptable
- Familiarity with MLOps and version control practices for production-scale model deployment and retraining
- Experience with data pipeline/ETL tooling (e.g., dbt, Cloud Dataflow, or equivalent) and integrating with enterprise systems (SAP, Salesforce, Anaplan)
- Strong analytical and problem-solving abilities, with demonstrated success translating data-driven insights into business impact
- Excellent communication and collaboration skills; ability to partner with technical and non-technical stakeholders
- FMCG, AlcoBev, or retail commercial analytics background is a plus

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