Senior Manager, Data Scientist - Growth Marketing

Expedia Group•Seattle, WA

2d•$173,000 - $277,000•Onsite

About The Position

Expedia Group is seeking a Senior Manager, Data Scientist - Growth Marketing. This role involves setting and owning the analytics and measurement strategy for SPP, leading a team of data scientists, and providing thought leadership on complex challenges such as cross-channel performance drivers, innovative measurement, and automation of data-heavy processes. The position requires a high degree of autonomy, the ability to define problems, and regular influence with stakeholders up to the VP/SVP level. The role includes direct management, coaching, and development of approximately three junior data scientists, setting technical standards, and prioritizing the team's roadmap based on business impact.

Requirements

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.
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.

Nice To Haves

Marketing, media, or performance-marketing measurement experience (paid social, programmatic, CTV, digital audio, paid app), and familiarity with incrementality and causal measurement.

Responsibilities

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.
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.
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.
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.
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.