Data Scientist I

Who we are:

At Preferred Travel Group, we champion the power of travel to inspire meaningful connections and enrich lives around the world. As a global family of brands and programs representing the finest independent hotels and resorts, we are united by a shared belief in authenticity, collaboration, and the value of independent spirit. Our culture reflects our ideology in action: people first, relationships at the center, and a commitment to creating lasting impact for our partners, our global community, and one another. When you join Preferred, you become part of a purpose-driven organization where ideas are welcomed, growth is encouraged, and your work contributes to shaping the future of travel.

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Why this role matters:

We are looking to hire an analytical, intellectually curious, business-minded, and results-driven Data Scientist who is passionate about using data to solve complex problems and uncover new opportunities. The Data Scientist is responsible for advanced analytical modeling, statistical analysis, and AI/ML-driven insights across PTG’s enterprise data platforms. This role partners closely with Data Engineering, BI Engineering, and business stakeholders to translate data into actionable intelligence, predictive models, and decision-support tools. By uncovering patterns, forecasting outcomes, and transforming complex data into meaningful insights, this position helps drive smarter business decisions, identify growth opportunities, and create measurable value across the organization.

Success in this role comes from a focus on developing scalable models and analytical frameworks that improve operational efficiency, revenue performance, and strategic decision-making. Success also requires the ability to translate complex datasets into clear, actionable recommendations, deliver reliable and repeatable analytical solutions, and continuously identify opportunities where data science and AI can create meaningful business impact.

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What you’ll deliver:

Data Science & Modeling

Develop, validate, and deploy statistical and machine learning models

Perform exploratory data analysis to identify trends, anomalies, and opportunities

Build predictive models (e.g., revenue forecasting, member retention, segmentation)

Design and evaluate experiments (A/B testing where applicable)

Develop feature engineering strategies across large datasets

Business & Analytical Translation

Translate business requirements into analytical models and measurable outputs

Define key metrics, KPIs, and analytical frameworks with stakeholders

Provide data-driven recommendations to support strategic initiatives

Support financial, marketing, and operations teams with advanced analytics

AI & Advanced Analytics

Support AI initiatives including recommendation models, automation, and agents

Apply advanced techniques such as:

Regression, classification, clustering

Time series forecasting

Natural language processing (where applicable)

Evaluate model performance and continuously optimize

Data Integration & Collaboration

Work closely with Data Engineers to ensure data readiness and pipeline integrity

Collaborate with BI Engineers to productionize models into reporting layers

Ensure models integrate into enterprise data architecture and workflows

Partner with QA to validate data accuracy and model outputs

Data Governance & Quality

Ensure data quality, consistency, and auditability of analytical outputs

Document methodologies, assumptions, and model logic clearly

Support compliance and audit requirements (SOC 2 alignment where applicable)

What you’ll bring to the team:

Programming:

Python or R (required)

SQL (advanced)

Machine Learning / Statistical Modeling:

scikit-learn, TensorFlow, PyTorch, or equivalent

Data Platforms:

Experience working with data warehouses (Snowflake, Azure, etc.)

Visualization:

Power BI, Tableau, or similar (for model output interpretation)

Experience with:

Cloud platforms (Azure, AWS)

Hospitality, loyalty, or revenue analytics (strong plus)

Data marts / Kimball methodology environments

Familiarity with:

Feature stores, MLOps, model deployment pipelines

AI-driven automation use cases

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What Would Make You Stand Out:

Having a bachelor’s or master’s degree in data science, mathematics, computer science or a related field along with 3-7 years of experience in data science or advanced analytics field plus experience working with large, complex datasets in production environments.

Where you’ll thrive:

You thrive in an analytical environment where statistical thinking, experimentation, and evidence-based decision-making are highly valued.

You are naturally curious and enjoy exploring data to uncover trends, patterns, and opportunities that others may miss.

You enjoy translating complex data into meaningful stories and actionable insights that influence business strategy and outcomes.

You balance independent problem-solving with strong collaboration, partnering across technical and business teams to deliver measurable impact.

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Our Working Culture:

With our in-office philosophy, our associates are expected to be in the office at least three days per week, supporting a healthy balance between in-person collaboration and flexible remote work. We take pride in our vibrant and inclusive culture, which thrives on meaningful connection, shared purpose, and cross-functional teamwork. In-office engagement plays a vital role in fostering spontaneous collaboration, accelerating innovation, and strengthening relationships across teams. It also provides valuable opportunities for mentorship, professional development, and a deeper sense of community.

Please note: While the current expectation is a minimum of three days per week in the office, this may evolve over time in alignment with business needs and our continued commitment to culture-building

Disclaimer: This description reflects the general nature and level of the role. It is not intended to be an exhaustive list of responsibilities or requirements.

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Salary:

$80,000 - $100,000, actual compensation within this range will be determined by multiple factors including candidate experience and expertise.