Director of Applied AI

IHG is seeking a Director, Applied AI for our Data Science team to lead efforts across forecasting, predictive modeling, optimization, ModelOps, and GenAI. In this role, you'll set the strategy, prioritize high-value opportunities, and build reusable capabilities that improve business decisions and deliver measurable impact. 

In this position, you'll act as a bridge to centralize efforts in three specific areas: Non-guest predictive modeling & forecasting - solutions supporting commercial planning, channels, sales, groups and events, hotel performance revenue, and operations, ModelOps-MLOps & LLMOps - shared capabilities and standards that help data science teams deploy, evaluate, monitor, and scale solutions reliably, and GenAI & agentic solutions -  analytics products and workflows that accelerate insight generation, reporting, and business productivity.

As the Director, Applied AI, you will manage a team and collaborate cross functionally with internal stakeholders. You will hire, lead, and develop a team of applied scientist and AI/ML specialist, inclduing managers and senior technical talent. In this role, you'll define the standards for scientific rigor, engineering practices, documentations, and business communication. You'll also build capability across teams through mentoring, technical reviews, and shared standards. You'll partner with senior leaders to communicate priorities, progress, risks, and business impact as well as coordinate with stakeholders across the buisness, product, technology as well as governance teams to support adoption and reliable operations.   

Your Day to Day

1. Applied AI strategy and portfolio leadership

• Own the Applied AI roadmap, with a clear process for intake, prioritization, delivery, and measurement.

• Translate business needs into opportunities for predictive modeling, forecasting, optimization, and GenAI.

• Set priorities across the portfolio based on business value, feasibility, data readiness, and adoption.

• Build reusable capabilities and consistent standards that help successful solutions move from experimentation into production.

• Partner with business leaders to define success measures and ensure solutions become part of operating workflows.

2. GenAI and agentic solutions

• Lead the development of GenAI analytics products that provide accurate, explainable answers from governed data products, including structured Data Analytics Products (DAPs).

• Develop conversational analytics and analyst copilots that help users explore “what changed?” and “why did this move?” through metric drivers, segment shifts, channel mix, and anomalies.

• Produce outputs grounded in data: tables, charts, narratives, hypotheses, and recommended next analyses.

• Develop insights from unstructured sources such as reviews, surveys, and call transcripts, including themes, sentiment, reasons for contact, and friction points.

• Build agentic workflows that improve productivity, automate recurring analytical work, and support business decision-making.

• Establish evaluation and release standards covering accuracy, grounding, traceability, consistency, and usability.

3. Non-guest predictive modeling and forecasting

• Lead applied AI and forecasting solutions for commercial planning, demand, revenue, operations, and performance management.

• Develop forecasting products for business outcomes such as revenue, occupancy, demand, and aggregate co-brand spend.

• Combine statistical forecasting methods with machine learning to capture seasonality, business drivers, events, promotions, and nonlinear relationships.

• Develop scenario and what-if capabilities that help stakeholders evaluate alternatives and understand uncertainty.

• Establish backtesting, baseline comparisons, performance monitoring, and appropriate prediction ranges.

• Deliver documented, reusable products that support recurring planning and decision-making.

4. MLOps and LLMOps

• Own shared ModelOps practices for machine learning and GenAI solutions within the data science organization.

• Establish foundations for versioning, testing, CI/CD, deployment, monitoring, alerting, and model refresh.

• Define operational readiness standards and monitor model performance, drift, calibration, and stability.

• Build GenAI evaluation capabilities covering groundedness, retrieval quality, output consistency, guardrails, and cost and latency.

• Work closely with P&T, Data Engineering, and vendor partners to implement and operate these capabilities.

• Reduce time to production and rework through reusable templates, tooling, playbooks, and coaching.

What We Need From You

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, MIS/IT, or a related field—or equivalent experience.
• 10+ years in applied machine learning, analytics engineering, or data science, including 3+ years leading managers or multidisciplinary teams.
• Demonstrated experience delivering production AI solutions and maintaining their performance over time.
• Strong forecasting experience in business settings, including back testing and communicating uncertainty.
• Experience building analytical products that achieve sustained adoption.

Technical and leadership capabilities

• Expert Python and SQL, with strong data modeling and analytics fundamentals.
• Experience with statistical and machine learning forecasting, hierarchical/time-series evaluation, and scenario modeling.
• Experience building GenAI solutions on structured enterprise data, with explainability and traceability to source data.
• Experience with unstructured text and speech analytics, including classification, theme extraction, and summarization.
• Strong understanding of MLOps and LLMOps, including evaluation, monitoring, observability, and operational readiness.
• Strong product judgment: clear problem framing, success measures, and adoption plans.

Travel - limited 10%

Location - Our hybrid work structure is an expectation of three (3) days a week in the ATLANTA office.  This expectation may be adjusted with the changing needs of the business.

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