Senior Manager, Data Science - eBay Live

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Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the Role and Team

eBay Live is eBay’s interactive live shopping platform where sellers and creators broadcast in real time and buyers interact through chat, bidding, and instant purchases. It combines entertainment, community, and commerce into an engaging, trust-supported way 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 high-profile, priority growth project with substantial potential—an opportunity to achieve measurable impact at marketplace scale.

As a Senior Manager of Data Science, you will oversee the analytics strategy and delivery across a full domain. You will establish and manage high-impact, resource-intensive projects that align colleagues with company goals. You will also craft the vision and standards for analytics within eBay Live.

You will set technical standards across teams. You will ensure methodological rigour. You will lead cross-org collaboration with product, engineering, business, and peer analytics. Together, you will ship scalable solutions and reusable building blocks.

This role drives the Data Science engine fuelling eBay Live’s rapid expansion. It sequences priorities, handles dependencies and risks, and implements processes that improve quality and speed throughout the domain to achieve measurable results.

What You Will Accomplish

You will be accountable for one of the following domains - establishing strategy, metrics taxonomy, and experimentation standards, directing others, bringing together cross‑functional teams, and advancing analytical rigour and outcomes.

Buyer Product Analytics: Lead the domain analytics strategy and roadmap; define a consistent metrics taxonomy and experimentation protocols; guide programs that promote ongoing progress in user acquisition, interaction, and conversion.

Seller Product & Seller Success: 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 regularly.

Trust & Safety: Own risk modelling and guardrail standards; align business/product/engineering on signals, definitions, and measurement; balance fraud prevention with good‑actor experience through evidence‑based decisions.

Data Foundation & Instrumentation: 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.

Business Performance: Lead the domain scorecard and governance of important metrics. Run weekly, monthly, and quarterly performance reviews. Drive executive-level decisions with clear, outcome-focused narratives based on shared metrics and experiments.

What You Will Bring

Demonstrates hands‑on technical depth by prototyping strategic tools and validating methods. Performs sophisticated analyses as needed. Proficient in SQL/Python, advanced experimentation, econometrics/time‑series, causal inference, dashboarding, and data modelling.

Domain expert & technical strategist: Deep command of the domain; select appropriate methods; ship production‑grade solutions that scale across teams and use cases.

As the organisation-level authority on analytical rigour, you own coding, analysis, and verification standards. You review and sign off on complex experiments and econometrics as the point of escalation.

Cross‑org collaboration: Guide alignment on a broad scale - develop processes for common definitions, clear prioritisation, and practical resolution of obstructive problems. Encourage a culture where choices are based on experimental data and long‑term results.

Influence at scale: Executive‑ready storytelling, whitepapers/strategy docs that build priorities and funding; trusted advisor who embeds analytics in planning and business reviews.

Leadership through leaders: Talent magnet and mentor; delegate and empower; set mechanisms and processes to track program delivery, partner happiness, and quantified business impact.

AI capabilities & innovation: Encourage a culture of innovation; promote high-impact ML/AI applications and the integration of new analytical tools throughout the field.

Experience: Advanced degree or equivalent experience in a quantitative field (for example, Statistics, Economics, Computer Science). Senior‑level track record leading multi‑team analytics programs; owning domain‑level strategy, standards, and delivery; and handling the highest-level resolution for complex methodological decisions. Seniority is assessed by demonstrated scope and outcomes rather than fixed years of experience.

Skills: Advanced experimentation, econometrics, and statistical modelling. Proficiency in SQL and Python with production-scale datasets. Experience in dashboarding and data storytelling. Developing analytics solutions with data and ML platform teams. Managing partnerships across product, engineering, and business.

Communication: Executive‑ready narratives that translate complex analyses into clear decisions; proven ability to align Director+ audiences and drive cross‑org adoption of analytics standards.

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