Staff 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 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 12 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