Senior Data Scientist, Sales & Guest Intelligence
Job Summary
The Data Scientist, Sales & Guest Intelligence plays a pivotal role in advancing a data-informed sales organization by transforming complex data into actionable insights that elevate guest engagement, personalization, and conversion.
Key Responsibilities
Develop and deploy predictive models to support sales strategy, including Conversion propensity models, Lookalike modeling for prospect expansion, and Guest lifetime value and repeat booking likelihood.
Apply statistical and machine learning techniques (regression, clustering, classification, time-series) to uncover actionable insights.
Design and execute A/B and multivariate tests to measure the causal impact of sales interventions, outreach strategies, and personalization initiatives.
Partner with Sales and Marketing teams to build a culture of rigorous experimentation, ensuring decisions are grounded in measured impact rather than prediction alone.
Design and refine segmentation frameworks that define high-value guest personas.
Translate segmentation into practical targeting strategies for Sales and Marketing teams.
Enable a more personalized sales approach by integrating insights into CRM workflows and agent interactions.
Support the development of tailored guest experiences based on behavioral and predictive signals.
Collaborate with cross-functional teams to align personalization strategies across channels.
Work across CRM, reservations, and marketing data to create a unified view of the guest.
Build and maintain analytical pipelines and scalable models that support business needs.
Partner with Data Engineering and BI teams to ensure data quality, accessibility, and consistency.
Establish and maintain standards for model monitoring, performance tracking, and drift detection to ensure production models remain accurate and reliable over time.
Conduct regular bias audits across segmentation and propensity models to ensure equitable and compliant guest targeting practices.
Document model design, assumptions, and limitations in a clear, accessible format to support transparency and organizational knowledge-sharing.
Translate complex analytical outputs into clear, concise insights for business stakeholders.
Present findings and recommendations to senior leadership to influence sales and commercial strategy.
Provide guidance and mentorship to junior data scientists and analysts, elevating the analytical capability of the broader team.
Contribute to the development of shared modeling standards, best practices, and reusable frameworks across the data science function.
Foster a collaborative, growth-oriented environment where technical rigor and commercial thinking are equally valued.
Competencies
Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field (Master's or PhD preferred)
5–8 years of progressive experience in data science or advanced analytics, with demonstrated expertise in machine learning, predictive modeling, and commercial application of data insights.
Strong proficiency in Python (e.g., Pandas, NumPy, scikit-learn) and SQL
Strong background in Customer segmentation and behavioral analytics, as well as predictive modeling and data mining
Familiarity with experimentation frameworks, A/B testing methodologies, and causal inference techniques
Experience with model monitoring, documentation, and governance practices
Strong ability to connect analytical outputs to commercial execution and sales decision-making
Experience translating customer intelligence into operational engagement strategies preferred
Preferred Experience
In luxury hospitality, travel, cruise, or high-touch sales environments
Familiarity with CRM platforms (e.g., Salesforce) and customer lifecycle data
Experience with cloud data environments (e.g., Snowflake, Sigma Computing, Spark, Databricks)
Exposure to personalization strategies, Clienteling, recommendation systems, or marketing analytics
Strong stakeholder management and executive communication skills
Demonstrated experience mentoring analysts or data scientists and contributing to team capability building
Experience in customer prioritization, recommendation systems, clienteling analytics, or commercial personalization strategies