Director, Data Science - Applied AI

IHG•Atlanta, GA

3d•$160,000 - $220,000•Hybrid

About The Position

IHG is seeking a Director, Data Science - Applied AI to lead strategy and delivery across forecasting, predictive modeling, optimization, ModelOps, and GenAI. The role sets priorities for high-value opportunities and builds reusable capabilities that improve business decisions and deliver measurable impact. It bridges and centralizes work in non-guest predictive modeling and forecasting; ModelOps, MLOps, and LLMOps standards and shared capabilities; and GenAI and agentic solutions for insight generation, reporting, and productivity. The Director manages, hires, and develops applied scientists and AI/ML specialists, including managers and senior technical talent. The role defines standards for scientific rigor, engineering practices, documentation, and business communication, and builds team capability through mentoring, technical reviews, and shared standards. It partners with senior leaders and coordinates with business, product, technology, governance, P&T, Data Engineering, and vendor stakeholders to establish priorities, communicate progress and risks, support adoption, and ensure reliable operations. The position owns an Applied AI roadmap and portfolio process for intake, prioritization, delivery, and measurement. It leads development of GenAI analytics products and agentic workflows, forecasting and predictive products for business planning and operations, and shared practices for deploying, evaluating, monitoring, and maintaining machine-learning and GenAI solutions. Success includes sustained adoption, measurable business impact, operational readiness, and reusable solutions that move from experimentation into production. The role is hybrid, with an expectation of working three days per week in IHG’s Atlanta office; this may change with business needs. Travel is limited to 10%.

Requirements

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 business forecasting experience, including backtesting and communicating uncertainty.
Experience building analytical products that achieve sustained adoption.
Expert Python and SQL skills, with strong data modeling and analytics fundamentals.
Experience with statistical and machine-learning forecasting, hierarchical and 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, including clear problem framing, success measures, and adoption plans.

Responsibilities

Own the Applied AI roadmap and establish processes for opportunity intake, prioritization, delivery, and measurement.
Translate business needs into predictive modeling, forecasting, optimization, and GenAI opportunities; prioritize work according to business value, feasibility, data readiness, and adoption.
Build reusable capabilities and standards that help successful solutions move from experimentation into production, and partner with business leaders to define success measures and integrate solutions into operating workflows.
Lead development of GenAI analytics products that deliver accurate, explainable answers from governed data products, including structured Data Analytics Products (DAPs).
Develop conversational analytics and analyst copilots that help users investigate changes and drivers, including segment shifts, channel mix, and anomalies.
Produce data-grounded outputs such as tables, charts, narratives, hypotheses, and recommended next analyses.
Develop insights from unstructured sources such as reviews, surveys, and call transcripts, including themes, sentiment, contact reasons, and friction points.
Build agentic workflows that improve productivity, automate recurring analytical work, and support business decision-making.
Establish GenAI evaluation and release standards for accuracy, grounding, traceability, consistency, and usability.
Lead applied AI and forecasting solutions for commercial planning, demand, revenue, operations, and performance management.
Develop forecasting products for revenue, occupancy, demand, and aggregate co-brand spend; combine statistical forecasting and machine learning to account for seasonality, business drivers, events, promotions, and nonlinear relationships.
Develop scenario and what-if capabilities, and establish backtesting, baseline comparisons, performance monitoring, and appropriate prediction ranges.
Deliver documented, reusable forecasting products that support recurring planning and decision-making.
Own shared ModelOps practices for machine-learning and GenAI solutions, including 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 for groundedness, retrieval quality, output consistency, guardrails, cost, and latency.
Work with P&T, Data Engineering, and vendor partners to implement and operate ModelOps and LLMOps capabilities.
Reduce time to production and rework through reusable templates, tooling, playbooks, and coaching.
Hire, lead, and develop applied scientists and AI/ML specialists, including managers and senior technical talent.
Define standards for scientific rigor, engineering practices, documentation, and business communication; build capability through mentoring, technical reviews, and shared standards.
Partner with senior leaders to communicate priorities, progress, risks, and business impact, and coordinate with stakeholders to support adoption and reliable operations.