Associate Director, Workplace/FM Data Science Manager

Job Description

The GWES Delivery team sits within the GSMG/GWES DS&I organization and is responsible for supporting Workplace Services and Facilities Management with advanced analytics, insights, and data-driven decision making. The team partners closely with key GWES functions including all Facilities Management operations, Integrated Facilities Management (IFM), Real Estate, finance, Workplace Experience & Smart Buildings, Asset management and sustainability.

We are seeking a Workplace/FM Data Science Manager to help lead the development of analytics solutions that deliver actionable insights and improved operational and business outcomes across the global workplace and facilities portfolio. Reporting to the Director, Data & Analytics – GWES, this role will work closely with GWES directors, regional leaders, and executive stakeholders to understand complex business challenges and design appropriate analytical approaches and models.

This is a hands-on leadership role, requiring strong expertise in extracting scientific and business value from data sources (operational, financial, sensor/IoT, service provider, RE data) using advanced analytics techniques including statistical modeling, optimization, simulation, and machine learning.

Key Responsibilities

• Develop a deep understanding of GWES stakeholder needs and priorities (Workplace Services, Facilities Management, Real Estate, IFM, Smart Buildings, Sustainability …) and translate them into scalable analytics solutions aligned with business objectives.

• Establish and maintain strong partnerships with global, regional, and site-level stakeholders, acting as a trusted analytics advisor.

• Lead the definition and delivery of complex analytics initiatives, working closely with GWES business and IT teams to select the right analytical methodologies and data approaches.

• Provide periodic performance and insight reporting to GWES Leadership, Regional Leads, Site leads  and Service Owners as required.

• Act as functional lead for a team of Business Performance Analysts and Data Analysts supporting GWES initiatives.

• Maintain awareness of data science and analytics market trends, particularly as they apply to workplace services, facilities operations, Real Estate, IoT/smart buildings, and service performance analytics, and proactively identify opportunities to increase value.

• Manage and develop a team of 1-2 data scientists, ensuring high-quality delivery, continuous skill development, and career growth within our Company.

• Build and nurture a community of practice for advanced analytics within GWES/GSMG, mentoring junior colleagues and contributing to a strong, scalable analytics capability.

• Contribute to innovation and experimentation, including ideation, proof-of-concepts, pilots, and measurable value realization.

• Communicate analytics insights clearly and effectively to business audiences, helping drive organizational adoption of data-driven decision making, and supporting the evolution from descriptive to predictive and prescriptive analytics.

• Promote a culture where analytics, modeling, and data are recognized as key differentiators for operational excellence and employee experience.

• Support the GWES Data & Analytics Lead in developing standardized performance reporting, dashboards, and insights for leadership and operational reviews as needed. Collaborate on performance management frameworks, including KPI definition, SLA monitoring, and continuous improvement initiatives.

• Anticipate future analytics demands related to people, technology, budget, and business strategy, and design scalable solutions to support evolving GWES priorities

Travel will be required (~15%)

Required Education:

• Bachelor Degree required. Advanced degree in data science, mathematics, statistics, or computer science or related fields preferred,

Required Experience:

• Minimum of 5 years of relevant delivery of complex analytical projects (industrialized, validated), across data & analytics, digital platforms, or enterprise systems.

• Demonstrated experience taking AI or advanced analytics use cases from prototype to production.

• Strong background in data & analytics solutions for operational domains

•  Solid software engineering foundation (Python preferred) and API‑based system