Senior Data Scientist

What we do:

GMG is a global well-being company retailing, distributing and manufacturing a portfolio of leading international and home-grown brands across sport, everyday goods, health and beauty, properties and logistics sectors. Under the ownership and management of the Baker family for over 45 years, GMG is a valued partner of choice for the world's most successful and respected brands in the well-being sector. Working across the Middle East, North Africa, and Asia, GMG has introduced more than 120 brands across 12 countries. These include notable home-grown brands such as Sun & Sand Sports, Dropkick, Supercare Pharmacy, Farm Fresh, Klassic, and international brands like Nike, Columbia, Converse, Timberland, Vans, Mama Sita's, and McCain.

What will you do:

We are hiring a Senior Data Scientist to partner with senior leaders across Retail Lines of Business: Everyday Goods (supermarkets) and Health & Beauty (pharmacies and beauty), leading the AI/ML roadmap to improve revenue, margin, customer outcomes, and operational efficiency. This is a hands-on individual contributor role with responsibility to mentor 1 junior resource, delivering analytics, classical ML, experimentation, and selected agentic/GenAI use cases from concept to measurable impact.

Role Summary:

- Own and drive the Retail AI/ML roadmap for Everyday Goods and Health & Beauty in partnership with LOB leaders.

- Deliver insights and ML solutions that drive revenue uplift, margin improvement, and operational efficiency.

- Productionize models using strong MLOps practices (monitoring, drift, retraining, reliability).

- Mentor a junior team member to deliver analysis, reporting, and project execution.

Responsibilities:

Business partnership & roadmap execution:

- Establish strong relationships with senior leaders across EDG and HnB; translate priorities into a clear AI/ML delivery pipeline.

- Identify, prioritize, and structure ML opportunities across retail operations, merchandising, assortment/range, promotions, supplier performance, personalization, and marketing effectiveness.

- Define success metrics, measurement plans, adoption approaches, and governance to ensure solutions are used and deliver results.

Advanced analytics & insight-to-action:

- Lead deep-dive analyses to uncover revenue and cost levers (e.g., promo effectiveness, OOS root causes, supplier performance trends, expiry/slow mover drivers).

- Create decision-ready insights with clear recommendations, confidence levels, and next actions - ensuring outputs translate into operational change.

Machine Learning, Personalization & Experimentation

- Build and productionize classical ML solutions such as forecasting, propensity models, segmentation, recommendation/ranking, anomaly detection, and optimization.

- Design and interpret experiments (A/B tests, controlled rollouts) to validate impact and improve model performance over time.

- Maintain strong model quality practices: leakage prevention, robust evaluation, calibration (where applicable), and bias checks where relevant.

Productionization & MLOps:

- Operationalize models into batch-first production pipelines with monitoring, drift detection, retraining strategies, and clear model/version management.

- Partner with data engineering/platform teams to ensure reliable feature pipelines, point-in-time correctness, and scalable scoring.

- Build runbooks and documentation to support stable operations and long-term maintainability.

How does success look like:

- Strong working relationships established with EDG and HnB leadership; a clear delivery cadence and backlog in place.

- Delivery of actionable insights that translate into measurable actions (e.g., improved promo ROI, reduced OOS, better supplier actions, reduced slow movers/expiry losses).

- At least 12 ML solutions operationalized with monitoring, retraining triggers, and performance reporting.

- A repeatable experimentation and measurement approach adopted for key initiatives.

- Junior mentee is enabled to deliver consistently and independently on defined workstreams.

Technical Competencies:

- 6+ years of proven experience as a Senior Data Scientist delivering measurable business outcomes, ideally in retail/e-commerce, CPG, supermarkets, or pharma/beauty contexts.

- Strong stakeholder management skills with the ability to influence senior leaders and translate ambiguity into a delivery plan.

- Strong hands-on DS/ML capability across analytics, modeling, and experimentation with industry best practices.

- Ability to lead projects independently end-to-end (problem framing modeling deployment measurement).

- MLOps experience: deployment patterns, monitoring, drift detection, retraining strategy, and model governance.

Technical(mandatory):

- Advanced Python for analytics/ML and strong SQL for data exploration, feature engineering, and validation.

- Strong foundations in statistics and machine learning: forecasting, classification/regression, clustering/segmentation, ranking/recommendations, evaluation metrics.

- Experimentation: A/B testing concepts, measurement planning, and correct interpretation of results.

- Production mindset: reproducibility, version control, testing, monitoring, and operational reliability for batch pipelines.

- Ability to communicate insights and model outputs clearly and drive adoption.

Technical(nice to have):

- Retail domain familiarity: promotions, pricing, inventory/OOS, supplier performance, expiry management, and category management.

- Experience leading reporting and insight routines with analysts and business stakeholders.

- Exposure to GenAI/Agentic AI patterns as complementary capabilities to classical ML use cases.

Qualification & Experience:

- Graduation or Masters in Statistics, Mathematics, Computer Science or equivalent

- 6+ years of proven experience as a senior/lead data scientist delivering business outcomes in complex environment