Jobs at GMG

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 Staff Data Scientist to partner with senior leaders in the Sports Line of Business and lead the end-to-end AI/ML roadmap for Sports across brand, e-commerce, retail operations, merchandising, assortments, and marketing. This is a senior individual contributor role with responsibility to mentor/lead 1–2 junior resources, driving high-impact analytics, machine learning, and experimentation from concept to production outcomes. Role Summary: - Own and drive the Sports AI/ML roadmap in partnership with Sports leadership. - Deliver insights and models that improve revenue, margin, and operational efficiency. - Build and productionize ML solutions (forecasting, personalization, optimization) with strong MLOps practices. - Lead/mentor a small team to deliver analysis, reporting, and ML initiatives. Responsibilities:Business partnership & roadmap ownership: - Act as a strategic analytics/AI partner to Sports leadership, translating business priorities into a clear AI/ML delivery plan. - Identify and prioritize opportunities across merchandising, pricing/markdowns, replenishment, demand forecasting, assortment planning, personalization, and retail performance. - Define success metrics, measurement approaches, and adoption plans to ensure outcomes are realized (not just models delivered). Advanced analytics & insight-to-action delivery: - Lead deep-dive analyses to uncover revenue uplift and cost reduction opportunities (e.g., drivers of sell-through, margin leakage, inventory health, promo effectiveness). - Establish robust reporting/insight routines that guide decision-making and actions across the Sports organization. - Guide junior team members in analysis structure, storytelling, and business-facing outputs. Machine Learning & Experimentation: - Build classical ML solutions such as forecasting, propensity/personalization, recommendation/ranking, segmentation, and optimization. - Design and interpret experiments (A/B tests, controlled rollouts), ensuring statistically sound measurement and learning loops. - Apply best practices in feature engineering, model evaluation, bias checks (where relevant), and calibration. Productionization & MLOps: - Operationalize models into production with monitoring, drift detection, retraining strategy, and clear model/version management. - Partner with data engineering/platform teams to ensure reliable data pipelines, point-in-time correctness, and scalable inference (batch-first). - Create model documentation and operational runbooks to support supportability and long-term ownership. Technical Competencies: - Strong stakeholder management: ability to influence senior leaders, shape priorities, and drive adoption. - Strong hands-on skills in analytics + classical ML and the ability to lead projects end-to-end. - Experience leading/mentoring junior team members and managing execution across multiple workstreams. - Demonstrated MLOps capability: deployment patterns, monitoring, drift detection, retraining strategy, and model governance. Technical(mandatory): - Advanced Python for analytics/ML and strong SQL for data exploration and feature building. - Solid foundations in statistics and ML: forecasting, classification/regression, segmentation, ranking/recommendations, model evaluation. - Experimentation: A/B testing concepts, measurement design, and interpreting results reliably. - Production mindset: reproducibility, version control, testing, and operational monitoring of models. - Ability to structure problems, define KPIs, and create measurement plans tied to business outcomes. Technical(nice to have): - Retail/Sports domain familiarity (merchandising, inventory, promotions, store performance). - Experience leading reporting/analysis teams and building stakeholder-ready insight products. - Exposure to GenAI/Agentic AI use cases (as part of the roadmap), alongside classical ML. Qualification & Experience:- Graduation or Masters in Statistics, Mathematics, Computer Science or equivalent- 8+ years of proven experience as a senior/lead data scientist delivering business outcomes in complex environments Show more Show less