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About This Role
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.
Salary Context
This $80K-$100K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Preferred Travel Group, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($90K) sits 53% below the category median. Disclosed range: $80K to $100K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Preferred Travel Group AI Hiring
Preferred Travel Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, US. Compensation range: $100K - $175K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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