Revenue Strategy Analyst

AfterShip•Los Angeles, CA

1d•$77,000 - $91,000•Remote

About The Position

As a Revenue Strategy Analyst, you will be the analytical engine behind AfterShip's GTM strategy, turning data into the decisions that shape how we grow. Reporting to the Senior Manager, Revenue Operations, you will partner across Sales, Marketing, Partnerships, Customer Success, Finance, and Data to uncover insights, optimize revenue-driving processes, and accelerate company OKRs through rigorous, data-informed thinking. This role is built for someone who thrives at the intersection of SaaS and eCommerce, leans into ambiguous business problems, and can translate complex datasets into clear narratives and strategic recommendations that leadership can act on. You will identify growth opportunities across GTM channels, sharpen reporting visibility, and drive high-impact operational initiatives that move revenue performance across the business. This is a highly cross-functional role that rewards strong analytical thinking, commercial acumen, and communication skill. The ideal candidate is comfortable engaging executive stakeholders, navigating incomplete or evolving datasets, and designing AI-enabled workflows that turn analytics into a competitive advantage rather than a reporting function.

Requirements

2 to 4 years of experience in Revenue Operations, Revenue Strategy, GTM Analytics, or related analytical roles, ideally within SaaS, eCommerce, or platform businesses
eCommerce business model experience strongly preferred (Shopify ecosystem, marketplace dynamics, or subscription commerce)
Required tooling: HubSpot (CRM, custom objects, workflows, reporting), Tableau (or equivalent BI), SQL
Demonstrated experience designing AI-enabled workflows that solve operational problems (not just using AI for personal productivity), including familiarity with prompt design, agentic workflows, or MCP-based integrations
Hands-on experience analyzing GTM performance across the full funnel: pipeline coverage, conversion rates, sales cycle velocity, forecast accuracy, win/loss, NRR, CAC payback, and segment-level performance
Proven ability to build dashboards, run ad hoc analyses, and translate findings into clear recommendations for Sales, Marketing, Partnerships, CS, and Finance stakeholders
Strong storytelling and executive communication, including the ability to present trade-offs, not just findings, to leadership
Comfort navigating ambiguity, scoping problems independently, and shipping work without heavy hand-holding
Bias toward systems thinking: when you see a recurring manual task, your instinct is to redesign the process, not just complete it faster
Track record of cross-functional collaboration across Sales, Marketing, Partnerships, Customer Success, Finance, and Data

Nice To Haves

Preferred tooling: workflow automation platforms (n8n, Zapier, Workato, or Make), Python for data analysis, exposure to a cloud data warehouse (BigQuery, Snowflake, Redshift)

Responsibilities

Analyze GTM channel and funnel performance to surface trends, growth levers, and operational gaps, then translate findings into prioritized recommendations
Partner with RevOps and GTM leadership to operationalize revenue strategies tied to company OKRs
Build executive-grade dashboards and self-serve reporting in Tableau and HubSpot, designed for decision-making
Run ad hoc strategic analyses across Sales, Marketing, CS, Partnerships, and Finance, including win/loss, churn, attribution, and segment profitability
Improve forecasting accuracy and pipeline predictability through analysis of conversion behavior, stage velocity, and deal slippage
Monitor core revenue metrics (pipeline coverage, conversion, velocity, NRR, GRR, CAC payback, quota attainment) and flag anomalies before they surface in QBRs
Translate complex datasets into clear executive narratives, owning the "so what" and the recommended action
Design scalable analytical frameworks and reporting processes that reduce one-off requests through better self-serve infrastructure
Build AI-enabled workflows to automate recurring analysis and accelerate insight delivery, treating AI as a system to design with
Investigate ambiguous datasets independently, scoping the question and shipping the answer without tight direction
Govern reporting consistency and data integrity across HubSpot, Tableau, and the data warehouse, ensuring shared metric definitions across teams