Senior Data Scientist, Sales & Guest IntelligenceJob SummaryThe 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.
This role combines applying advanced analytics and machine learning with a strong commercial orientation, translating guest intelligence into practical sales prioritization, engagement optimization, and conversion improvement. In the role, you will work across diverse customer datasets to build predictive models, develop meaningful guest segments, and enable a more personalized, insight-led approach to sales.
Key ResponsibilitiesAdvanced Analytics & Modeling
- 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.
- Perform data exploration, feature engineering, and model evaluation to ensure robust and scalable outputs.
Experimentation & Causal Inference
- Design and execute A/B and multivariate tests to measure the causal impact of sales interventions, outreach strategies, and personalization initiatives.
- Apply uplift modeling and causal inference techniques to distinguish actionable drivers of conversion from correlative signals.
- Partner with Sales and Marketing teams to build a culture of rigorous experimentation, ensuring decisions are grounded in measured impact rather than prediction alone.
Guest Segmentation & Behavioral Insights
- Design and refine segmentation frameworks that define high-value guest personas.
- Analyze guest behaviors across the lifecycle to identify patterns that influence booking decisions.
- Translate segmentation into practical targeting strategies for Sales and Marketing teams.
Sales Intelligence & Strategy Enablement
- Partner with Sales leadership to embed data-informed insights into day-to-day decision making.
- Identify drivers of conversion performance, lead quality, and engagement effectiveness.
- Develop "next-best-action" recommendations to optimize outreach timing, messaging, and prioritization.
- Support development of lead prioritization frameworks and next-best-action logic across inbound and proactive engagement.
- Identify speed-to-lead, sequencing, and personalization opportunities that materially improve conversion.
Personalization & Commercial Impact
- 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.
Data Integration & Analytical Infrastructure
- 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.
Model Governance & Responsible AI
- 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.
Stakeholder Communication & Influence
- Translate complex analytical outputs into clear, concise insights for business stakeholders.
- Present findings and recommendations to senior leadership to influence sales and commercial strategy.
- Serve as a key bridge between technical data teams and commercial functions.
Technical Leadership & Mentorship
- 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.
CompetencyEducation
- Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field (Master's or PhD preferred) and/or 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.
Skills & Experience
- 5+ years of experience in data science, advanced analytics, or related roles
- Proven experience applying machine learning and statistical modeling to business problems
- 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
- Ability to translate technical findings into actionable business insights
- 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
Work Environment- Full-time
- Office-based with Hybrid flexibility
- International Travel as needed
