Project Manager, Merchandising
About this role
Managing outcome-driven projects end-to-end forms the core of daily operations, involving the scoping of timelines, setting of success metrics, and independent execution of initiatives like freelancer content batches and supplier data fixes. The Merchandising team tackles open-ended quality challenges related to supplier and traveler-facing data, including activity attributes, categories, and location data, while leveraging artificial intelligence to scale these efforts across the inventory. Candidates who thrive in this environment possess hands-on experience orchestrating large language model tools, strong SQL and notebook-based data capabilities, and the ability to define quality standards for structured content.
What you will do
- Own outcome-driven projects end-to-end by establishing scope, timelines, and success metrics upfront before running execution independently.
- Define quality standards for the domain by building and validating datasets alongside precision and recall targets.
- Configure and orchestrate LLM-based workflows to build AI-powered quality checks that replace manual review at scale.
- Partner with senior stakeholders to scope ambiguous problems and prioritize initiatives using data-backed impact.
- Present solutions with clarity and conviction while resolving alignment issues proactively.
What you need
- Demonstrated experience owning projects end to end from fuzzy operational problems to delivered results.
- Hands-on experience configuring and orchestrating AI and LLM tools such as OpenAI and Anthropic APIs to automate quality checks or build datasets.
- Comfort using SQL and notebook-based data tools like Databricks to query and validate data independently.
- Ability to use data for prioritization, defining success metrics, and validating hypotheses.
- Fluency in English with clear written and verbal communication skills.
Nice to have
- Experience building or fine-tuning LLM prototypes
- Background in content quality, supplier operations, or marketplace quality
- Familiarity with precision/recall frameworks and quality evaluation methods
Benefits
- Annual personal growth budget and mentorship programs
- Work from anywhere in the world for 30 days per year
- Hybrid working approach with three days in office and two days optional at home
- Quarterly team event budget
- Access to leading AI tools with resources and budget
- Monthly transportation and fitness budget
Skills and tech stack
Questions candidates ask
What is the remote work or office policy?
The position follows a hybrid working approach with three days of in-office collaboration on Monday, Tuesday, and Thursday, combined with two days of optional at-home focus time. Additionally, employees can work from anywhere in the world for 30 days per year.
What tools and technologies will I use?
The toolkit includes SQL and notebook-based data tools like Databricks to query and validate data independently. You will also use AI and LLM tools such as OpenAI and Anthropic APIs to configure and orchestrate automated quality checks.
What does the day-to-day work involve regarding content?
This is a data and process role focused on structured content and quality metrics rather than copywriting or editorial tasks. You will tackle challenges on supplier and traveler-facing data, think activity attributes, categories, and location data, and turn them into scoped projects.