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About This Role
At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.
Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors\-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together\-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.
Senior Manager, Data Scientist \- Growth Marketing
A senior data science leader who sets and owns the analytics and measurement strategy for SPP, leads a team of data scientists, and provides thought leadership on the most complex, ambiguous problems facing the channel — cross\-channel performance drivers, innovative measurement, and automation of data\-heavy recurring processes. Operates with a high degree of autonomy, defines the problems worth solving rather than executing pre\-scoped tasks, and regularly influences stakeholders through VP/SVP level. Directly manages, coaches, and develops a team of \~3 data scientists (each one level junior), setting technical standards and prioritizing the team’s roadmap against business impact.
Experience \& Qualifications:
- PhD, Masters, or Bachelors, preferably in a quantitative or scientific field (Mathematics, Statistics, Economics, Computer Science, Physics, or similar).
- 7\+ years in data science / advanced analytics, including 2\+ years directly managing or formally leading data scientists or analysts. Comparable leadership experience will be considered in lieu of formal management tenure.
- Proven track record of delivering data\-driven insights and measurement solutions that changed business decisions or drove performance improvement, across multiple domains and senior stakeholders.
- Advanced, production\-grade experience with Python, R, or SQL for analysis, transformation, modeling, and visualization of large datasets.
- Preferred: marketing, media, or performance\-marketing measurement experience (paid social, programmatic, CTV, digital audio, paid app), and familiarity with incrementality and causal measurement.
Functional / Technical Skills
- People leadership: coaching, performance management, hiring, career development, and technical mentorship.
- Strategic thinking and the ability to operate and lead a team through ambiguity.
- Advanced statistics: frequentist and Bayesian methods, experimental design, regression, causal inference.
- Machine learning theory and applied practice.
- Data engineering fundamentals: pipelines, automation, scalable dashboards.
- Stakeholder influence and communication at senior (Director / VP / SVP) levels.
- Storytelling and data visualization for both technical and non\-technical audiences.
- Strong business acumen and domain judgment.
In this role, you will:
People Leadership \& Team Development
- Directly manages a team of \~3 data scientists (each one level junior), owning hiring, onboarding, performance management, and career development.
- Sets technical standards, review practices, and ways of working for the team; establishes code review, reproducibility, and documentation norms.
- Coaches team members on statistical technique, modeling, experimentation, and stakeholder communication, and creates development paths that stretch each individual.
- Allocates team capacity against a prioritized roadmap, balancing strategic bets, BAU measurement, and stakeholder demand; protects the team from low\-value order\-taking.
- Builds a collaborative, transparent, and inclusive team culture; represents the team’s work and needs to senior leadership.
Strategy \& Handling Ambiguity
- Defines the analytics and measurement strategy for SPP and translates ambiguous, loosely\-defined business goals into structured analytical programs with clear objectives and phased delivery.
- Frames complex, open\-ended business problems as tractable analytics problems and sequences them into manageable workstreams for the team.
- Makes independent judgment calls on scope, method, and level of effort with limited direction; solves for the underlying objective, not the literal ask.
- Anticipates emerging measurement and performance questions before they are raised and proactively shapes the team’s agenda around them.
Thought Leadership — Cross\-Channel Performance \& Innovative Measurement
- Provides thought leadership on cross\-channel performance drivers across paid social, programmatic display \& video, CTV, digital audio, and paid app, spanning BEX, HCOM, and VRBO.
- Advances the measurement toolkit — incrementality, causal impact, geo experiments, media mix / multi\-touch approaches, uncertainty\-aware and multi\-armed bandit methods — and selects the right technique for each question, articulating trade\-offs between simpler and more complex approaches.
- Critically evaluates new methods, tools, and datasets, pilots promising ones, and scales what works into repeatable practice.
- Partners with Machine Learning / Data Science and measurement teams to validate and scale models for maximum business value.
- Sets the standard for interpreting model output correctly, iterating, and distinguishing statistically significant readouts from exploratory analysis.
Automation \& Data Engineering
- Owns the automation strategy for the team’s data\-heavy recurring processes — weekly, monthly, and quarterly reporting (WBR / MBR / QBR) — reducing manual effort and freeing capacity for higher\-value analysis.
- Directs the design of scalable dashboards and scheduled reporting covering multiple scenarios (geo, web and app, brand), and empowers stakeholders with self\-serve access and training.
- Guides the team in building shareable, efficient, well\-documented code and data pipelines; champions reproducibility via tools such as GitHub and Confluence.
- Applies and enforces best practices for data quality, query cost and performance optimization, and integration across disparate sources.
- Writes and reviews advanced SQL (views, tables, partitions, window functions such as RANK() OVER / PARTITION BY) and works fluently across SQL flavors and querying tools; knows the most important data sources for SPP and the wider business and how to unblock data issues to resolution.
Stakeholder Engagement \& Communication
- Regularly interacts with and influences stakeholders through VP/SVP level; builds trust and works transparently across the business.
- Independently articulates project goals, methodology, caveats, and conclusions to technical and non\-technical audiences, tailoring executive summaries and presentations to the audience’s goals and technical level.
- Tells a clear, concise story and presents insight rather than data; creates the right artifacts (technical documentation, presentations, executive summaries) for the right forum.
- Identifies and engages domain experts and stakeholders early to sharpen the business question, feature selection, and relevance of outputs.
- Not easily deterred by organizational barriers to sharing results or effecting change; follows through to impact.
The total cash range for this position in Seattle is $173,000\.00 to $242,500\.00\. Employees in this role have the potential to increase their pay up to $277,000\.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual’s knowledge, skills, and experience. Pay ranges may be modified in the future.
Benefits and perks
Expedia Group offers benefits and perks designed to support employees and their families, including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and International Airlines Travel Agent Network (IATAN) membership. Learn more about life at Expedia Group at https://careers.expediagroup.com/life .
Accommodation requests
Expedia Group is committed to providing an inclusive and accessible recruiting experience. If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at https://expedia.service\-now.com/askeg?id\=job\_accommodation .
About Expedia Group
Expedia Group includes three flagship consumer brands \- Expedia, Hotels.com, and Vrbo \- along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.
Important notice
Employment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is https://careers.expediagroup.com/jobs/ .
Equal Opportunity
Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other characteristic protected by law. This employer participates in E\-Verify. The employer will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS) with information from each new employee's I\-9 to confirm work authorization.
Salary Context
This $173K-$277K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Expedia 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($225K) sits 17% above the category median. Disclosed range: $173K to $277K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Expedia Group AI Hiring
Expedia Group has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span Seattle, WA, US, Austin, TX, US, San Jose, CA, US. Compensation range: $196K - $299K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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