Role Summary : The Lead Data Scientist for Information Management & Analytics sets direction for advanced analytics and machine learning that improves business performance and customer experience. This role leads the design and evolution of algorithms and analytical products that deliver real-time business recommendations and customer-facing recommendations across web and mobile. Primary domains include Inventory Management, Sales Efficiency, Marketing, and Owner Satisfaction, with extensions into Digital Analytics, Pricing, Portfolio performance, and related areas. The Lead partners with ML Ops, Operational Reporting, and IT to define scalable patterns for model lifecycle management, governs quality and risk for production models, and mentors data scientists while aligning roadmaps to enterprise priorities.

Work Hours and Location : Ability to work on Eastern time zone is required, on sight at GCC from 8-12 EST required. Can work from home 12-5 EST with good internet connection

Position Summary :

- Define and prioritize the analytics and ML roadmap; translate business strategy into a portfolio of models, experiments, and analytical products with clear success metrics.

- Lead end-to-end delivery of complex ML and statistical solutions - from problem framing and data strategy through deployment, monitoring, and iteration - in partnership with ML Ops and engineering.

- Own architecture and standards for forecasting, recommender systems, and predictive analytics; ensure reproducibility, documentation, and operational readiness.

- Coach and develop individual contributors; review designs and code; foster a culture of rigorous experimentation, responsible use of data, and continuous improvement.

- Represent IMA analytics with senior business and technology stakeholders : set expectations, manage trade-offs, and communicate implications of analytical work for operations and financial outcomes.

- Drive ad-hoc and strategic analyses for high-impact decisions; elevate quality of insight and narrative across the teams deliverables.

- Lead or significantly contribute to cross-functional initiatives that bridge operational analytics to financial and customer outcomes; perform other duties as assigned.

Technical Expertise :

- Advanced proficiency in Python, R, and SQL for scalable data manipulation, feature engineering, and model development.

- Deep knowledge of machine learning (recommender systems, forecasting, predictive modeling) and when to apply classical vs. modern approaches; experience with model validation and governance in production.

- Strong command of data visualization and storytelling (e.g., Power BI or equivalent) for executive and operational audiences.

- Hands-on experience with cloud platforms and ML Ops : deployment patterns, monitoring, retraining, and integration with enterprise data and application stacks.

- Expertise in code management, CI/CD, and collaboration tooling (Git, Azure DevOps, etc.); ability to define branching, review, and release practices for analytical codebases.

Analytical & Statistical Skills :

- Expert-level statistical modeling, causal thinking, hypothesis testing, and experimental design; ability to guide others on methodology and interpretation.

- Experience leading work with large, messy datasets; establishing data quality standards and analytical controls across projects.

- Familiarity with digital analytics (e.g., Google Analytics) and customer behavior analysis at scale.

Leadership & Communication :

- Proven ability to lead without heavy hierarchy : influence priorities, resolve ambiguity, and deliver through others as well as through individual technical contribution.

- Excellent written and verbal communication for technical documentation, executive summaries, and stakeholder workshops.

- Track record of productive partnership with ML Ops, IT, product, and business teams on shared roadmaps and operating models.

Preferred Skills :

- Experience in pricing optimization, inventory management, marketing analytics, or hospitality/travel-related analytics.

- Background with real-time recommendation systems, personalization, and API-driven model consumption.

- Experience with Databricks asset bundles, Azure DevOps pipelines, and enterprise data platforms.

- Prior leadership or technical lead experience (formal or informal) for data science or analytics teams.

Education :

Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, or related field required.

Masters degree or PhD in a quantitative discipline strongly preferred for depth in methods and leadership credibility.

Experience :

- 8+ years of experience in data science, machine learning, or advanced analytics, including substantial hands-on work in model development and production environments.

- At least 2-3 years in a lead, principal, or comparable role with accountability for technical direction, stakeholder outcomes, and mentoring others (formal people management a plus but not required).

- Prior experience in digital analytics, customer experience optimization, or business performance modeling is a plus.