Saks Global is the largest multi-brand luxury retailer in the world, comprising Saks Fifth Avenue, Neiman Marcus, Bergdorf Goodman, Saks OFF 5TH, Last Call and Horchow.

Its retail portfolio includes 70 full-line luxury locations, additional off-price locations and five distinct e-commerce experiences.

With talented colleagues focused on delivering on our strategic vision, The Art of You, Saks Global is redefining luxury shopping by offering each customer a personalized experience that is unmistakably their own.

By leveraging the most comprehensive luxury customer data platform in North America, cutting-edge technology, and strong partnerships with the world's most esteemed brands, Saks Global is shaping the future of luxury retail.

Saks Global Properties & Investments includes Saks Fifth Avenue and Neiman Marcus flagship properties and represents nearly 13 million square feet of prime real estate holdings and investments in luxury markets.

About The Role :

You will guide and inspire a team of data scientists to deliver high-impact solutions for all the Saks global banners.

Whether its reviewing end-to-end projects, mentoring team members, or driving innovation in DS practices, your work helps us align data science with business goals and scale solutions that make a measurable impact.

Responsibilities :

- Guide and review project pipelines, ensuring quality and timely delivery.

- Manage and mentor DS/SDS, providing career guidance and fostering innovation.

- Take responsibility for the teams deliverables and own functional outcomes.

- Coordinate with cross-GCC verticals, stakeholders, and business partners to socialize and align solutions.

- Define goals, and promote out-of-the-box experimentation aligned with business impact.

- Drive synergy among direct reports and ensure team cohesion.

Qualifications :

- 10 - 12 years of experience in implementing DS models with emphasis on.

Required Skills :

- Strong DS technical foundation with the ability to enforce and promote best practices.

- Proven experience in people management coaching, performance reviews, and growth planning.

- Familiarity with advanced ML/DL methods and ability to evaluate when/where to apply them.

- Experience in scaling models from POC to production and ensuring robustness of deployed systems.

- Ability to review code, pipelines, and architecture for quality, efficiency, and maintainability.

- Strong grounding in ML lifecycle management (data prep, feature engineering, experimentation, deployment, monitoring).

- Strategic thinking to align technical choices with business impact and maximize ROI.

- Experience working cross-functionally with engineering, product, and business teams to ensure adoption and scalability.