Job Description
About the RoleRoamless has scaled rapidly, delivering a trusted product used by over 2 million travelers across 200+ countries. As we transition into our next phase of optimization, we are looking to establish our very first dedicated product analytics practice. Currently, there is no pre-existing experimentation framework, analytics playbook, or rigid process in place—meaning you will be writing the playbook from scratch.
As our first dedicated Product Analytics hire, you will define how we run experiments, select our analytics stack, make data accessible across the product organization, and build the foundational frameworks that guide every future product decision. This is not a maintenance role where you inherit existing dashboards; you will architect how we measure success, structure experimentation, and leverage data-driven insights.
Reporting directly to Elisa (Head of Product) and collaborating closely with engineering, design, and product teams, your work will directly optimize the entire user journey—from onboarding and pricing to engagement across our mobile app, website, and partner portal.
Responsibilities
What You’ll Do
As our foundational product analytics leader, you will act as a strategic partner to product managers, engineers, and designers across the entire product development lifecycle, using data to shape decisions from ideation to execution.
End-to-End Experimentation: Design, execute, and oversee A/B tests from start to finish—defining hypotheses, calculating sample sizes, monitoring live experiments, analyzing results, and delivering clear, actionable recommendations to the product team.
Framework Development: Build our experimentation framework completely from scratch, establishing best practices for how we design, run, and learn from experiments across our mobile app, website, and partner portal.
PLG Metric Optimization: Analyze user behavior and core product metrics to uncover high-impact growth opportunities across Product-Led Growth (PLG) pillars, including user activation, monetization, and retention.
Success Metric Definition: Define and track key product performance indicators, ensuring the entire team has a shared understanding of what success looks like and how it is measured.
Actionable Insights Generation: Translate complex data insights into practical product recommendations that directly elevate the customer experience.
Data Culture & Advocacy: Cultivate a strong data-driven culture by mentoring the product team on experimental design, results interpretation, and data-informed decision-making.
Requirements
What You’ll BringCore Experience & Background
Professional Experience: 5+ years of experience as a Product Analyst (or in a similar analytical role) at a tech company, working closely alongside product, design, and engineering teams.
Experimentation Expertise: Hands-on experience designing and running large-scale A/B tests—covering sample sizing, statistical significance, and common pitfalls—with a proven ability to translate results into clear product actions.
Framework Establishment: Direct experience setting up experimentation frameworks and analytics best practices, ideally from scratch or within an early-stage, high-growth company.
Technical Skills & Tools
Database & Querying: High proficiency in SQL, with the ability to independently write complex queries to extract and analyze large datasets.
Business Intelligence: Demonstrated experience building dashboards using BI platforms such as Tableau, Looker, or equivalent tools.
Product Analytics: Hands-on proficiency with product analytics tools like Mixpanel, Amplitude, or similar platforms.
Competencies & Culture Fit
Communication Skills: Exceptional ability to translate complex data into digestible product insights that non-technical stakeholders can easily understand and act upon.
Resourcefulness: A proactive, self-starting mindset that thrives in ambiguous environments where processes are not fully defined, bringing clarity to the team.
Startup Agility: Previous experience in fast-paced startup or scale-up environments, with a willingness to wear multiple hats.
Nice-to-Haves
Proficiency in Python or R for advanced statistical analysis.
Experience working with data warehouses and databases such as MongoDB, BigQuery, Snowflake, or Google Analytics.
Industry background in travel, digital marketplaces, or subscription business models.
Experience analyzing user behavior across both mobile and web applications.
