Senior Manager eBay Live Data Science

eBay•San Jose, CA

8h•Remote

About The Position

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all. About eBay Live eBay Live is eBay’s interactive live shopping platform where sellers and creators broadcast in real time and buyers participate through chat, bidding, and instant purchases. It combines entertainment, community, and commerce into an engaging, trust-supported method to explore and shop. Join us to develop the analytics and AI foundation that drives discovery, engagement, and quality moderation throughout Live. It’s a prominent, priority growth initiative with considerable potential—an opportunity to create tangible impact at marketplace scale.

Requirements

Demonstrates hands-on technical depth by prototyping strategic tools and validating methods.
Dives into complex analyses when needed.
Proficient in SQL/Python, advanced experimentation, econometrics/time-series, causal inference, dashboarding, and data modeling.
Deep command of the domain; select appropriate methods; ship production‑grade solutions that scale across teams and use cases.
Serve as the organization’s authority on analytical rigor. Own and enforce coding, analysis, and verification standards.
Review and approve complex experiments and econometrics as the escalation point.
Lead alignment at scale - develop mechanisms for shared definitions, transparent prioritization, and pragmatic handling of blocking issues.
Foster a culture where decisions are grounded in experimental evidence and long‑term outcomes.
Executive-ready storytelling, whitepapers/strategy docs that build priorities and funding; trusted advisor who embeds analytics in planning and business reviews.
Talent magnet and mentor; delegate and empower; set mechanisms and processes to track program delivery, partner happiness, and quantified business impact.
Cultivate a culture of innovation; advocate for impactful ML/AI applications and the integration of new analytical tools across the domain.
MS/PhD in a quantitative field (e.g., Statistics, Economics, CS) or equivalent experience; typically 9+ years in analytics/data science.
Extensive track record leading multi‑team analytics programs and owning domain‑level strategy, standards, and delivery; point‑of‑escalation for complex methodological decisions.
Advanced experimentation/econometrics/statistical modeling; SQL/Python; dashboarding; architecture‑aware analytics solutioning; collaborator management across product/engineering/business.
Executive-ready narratives that translate complex analyses into decisions; proven ability to align Director+ audiences and drive cross‑org adoption of analytics standards.

Responsibilities

Own one of the following domains: setting strategy, metrics taxonomy, and experimentation standards.
Lead through others, align cross‑functional teams, and improve analytical rigor and outcomes.
Manage domain analytics strategy and roadmap; define a unified metrics taxonomy and experimentation criteria; guide programs that promote continuous progress in discovery, engagement, and conversion.
Define the growth analytics agenda across acquisition, onboarding, listing quality, conversion, and retention; govern causal measurement and experimentation; ship reusable measurement assets and instrumentation that scale.
Lead category and market selection and sequencing.
Run pilots to reduce launch risks.
Align partners on metrics taxonomy, definitions, and instrumentation.
Track expansion outcomes with clear cadences.
Own risk modeling and guardrail standards; align business/product/engineering on signals, definitions, and measurement; balance fraud prevention with good‑actor experience through evidence‑based decisions.
Set event/metric taxonomies, instrumentation quality, and coding/verification standards; build semantic layers/templates; align architecture and data products across teams for consistency and speed.
Own the domain scorecard and governance of critical metrics.
Run weekly, monthly, and quarterly performance reviews.
Drive executive-level decisions with clear, outcome-focused narratives based on shared metrics and experiments.