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About This Role
arrivia is hiring a Senior Data Scientist to bring rigorous experimentation, predictive modeling, and decision science to our highest-value business problems. This is a new role on our growing data team, reporting to the VP of Data and AI Platform.
Your focus starts with marketing technology and marketing effectiveness, including funnel measurement, attribution, audience intelligence, personalization, and experimentation. From there, your work extends into enterprise priorities across travel products, customer experience, finance, and operations.
This is a senior individual contributor role with real autonomy. You will own a prioritized backlog from problem framing through deployment, decide which methods fit the question, and say plainly when the data will not support the conclusion someone wants. The work directly influences marketing investment and customer targeting across our brands.
What You'll Own
- Marketing Effectiveness: Lead scientific analysis across acquisition, lifecycle, audience strategy, channel performance, conversion, retention, and revenue.
- Experimentation: Design and evaluate randomized tests, holdouts, quasi-experimental studies, and incrementality analysis, with clear hypotheses, guardrails, power analysis, and decision criteria.
- Predictive Modeling: Build models for propensity, segmentation, next best action, churn, lifetime value, and campaign optimization that deliver measurable value.
- Attribution and Media Optimization: Connect channel, session, customer, and booking identifiers, separate factual reporting from modeled attribution, and feed offline conversions back to advertising platforms with privacy and consent built in.
- Funnel Measurement: Establish consistent metrics across brands and channels, from reach and engagement through bookings, revenue, and ROI.
- Data Quality and Instrumentation: Assess data across experimentation, customer, marketing automation, analytics, advertising, call center, and transactional sources, document limitations before results reach executives, and define instrumentation and identity requirements with engineering.
- Reusable Analytical Assets: Create datasets, features, and evaluation methods that enable trusted self-service analysis.
- Executive Communication: Explain findings, uncertainty, and tradeoffs in business language, linking recommendations to revenue and customer outcomes.
- Coaching and Standards: Coach analysts and partners on sound methods, reproducibility, and responsible AI.
What You'll Bring
- 7+ years applying data science, experimentation, statistics, or machine learning to complex business problems.
- Demonstrated expertise in experimental design and causal inference, plus predictive modeling, segmentation, forecasting, or optimization in production or decision-making settings.
- Advanced SQL and Python or R, with reproducible, production-quality analytical workflows.
- Experience with large, complex customer, digital behavior, campaign, or transaction datasets.
- Strong communication skills, including explaining methods and uncertainty to technical and executive audiences.
- Bachelor's degree in a quantitative field such as data science, statistics, mathematics, computer science, economics, or engineering. Master's or doctorate preferred. An equivalent combination of education and experience will be considered.
Nice to Have
- Marketing science, advertising technology, or digital product analytics experience, including hands-on work with Statsig, Tealium, Marketo, GA4, advertising platforms, Power BI, or Microsoft Fabric.
- Identity resolution, multi-touch attribution, marketing mix modeling, incrementality testing, or offline conversion feedback, including privacy-aware measurement under GDPR and CCPA.
- Experience deploying and monitoring models with platform engineering teams, and establishing model governance and analytical standards.
- Background in travel, ecommerce, membership, or loyalty businesses with blended online and offline customer journeys.
Benefits & Perks
- Unlimited PTO
- Exclusive employee travel rates
- Travel discounts through arrivia programs
- Medical, dental, and vision insurance
- 401(k) with company participation
Who We Are
arrivia powers travel loyalty and rewards programs for some of the world's leading brands. We help millions of travelers book memorable experiences while delivering the technology, supply, and service our partners build their programs on.
We are investing in growth and in the platform that drives it. Modern technology, data, and AI are changing how travel is sold and serviced, and we are building the systems and the teams to lead that shift rather than follow it.
Our Core Values
Here at arrivia we…
- Stay Curious - Explore new challenges and make space to learn, grow and improve
- Keep it Real - Earn trust through open, honest and clear communication
- Own it - Seek ways to make an impact and take action
- Win Together - Create a culture of connection and inclusion where everyone can be their best
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At arrivia, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
arrivia AI Hiring
arrivia has 1 open AI role right now. They're hiring across Data Scientist. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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