• Review and challenge existing A/B testing processes, identify methodological limitations, and recommend improvements to strengthen experimentation practices.
• Develop and implement a robust experimentation framework covering hypothesis formulation, metric selection, statistical power calculations, test duration, and decision-making criteria.
• Design, review, and validate experiment plans with Product and business teams before launch, ensuring appropriate methodologies and reliable measurement approaches.
• Analyze completed experiments, interpret statistical results, communicate limitations and uncertainties, and recommend clear next steps based on findings.
• Continuously improve experimentation methodologies and analytical tools while keeping processes accessible enough to encourage autonomy across teams.
• Analyze data throughout the customer journey, including acquisition, conversion, product usage, payments, and retention, to identify growth opportunities and revenue drivers.
• Investigate conversion funnels, customer cohorts, and user behavior to identify friction points and develop high-impact product improvement hypotheses and experiments.
• Collaborate with Payment teams to analyze transaction performance, investigate payment-related risks, and design experiments to improve payment success rates.
• Evaluate customer value across different cohorts, acquisition channels, products, and customer segments, taking data maturity into account.
• Improve customer lifetime value (LTV), customer acquisition cost (CAC), and payback period analyses to support marketing investment and broader business decisions.
• Work with Data Engineering specialists to improve the use of customer value signals within advertising platforms and measure the impact of these optimizations.
• Translate business and leadership questions into appropriate analytical approaches, statistical analyses, and experimentation protocols.
• Collaborate with Data Analysts and Data Engineers to improve data reliability, ensure consistency, and establish shared definitions for key business metrics.
• Present analytical findings, statistical uncertainties, and actionable recommendations clearly to both technical and non-technical stakeholders.

Requirements:

• Between 3 and 5 years of professional experience in Product Data Science, Growth Data Science, or a comparable analytical role.
• Previous experience working with high-traffic digital products or large datasets, preferably in SaaS, subscription-based businesses, e-commerce, or similar environments.
• Hands-on experience designing, implementing, and analyzing experiments, with demonstrated success using data to improve product performance or business outcomes.
• Strong statistical and inferential knowledge, with the ability to select appropriate analytical methods based on the business question and available data.
• Practical expertise in experimental design and analysis, including hypothesis formulation, statistical power calculations, multiple testing, metric selection, and cohort maturity.
• Advanced SQL skills and proficiency in Python for data analysis, statistical modeling, and experimentation.
• Strong understanding of product analytics, including conversion funnels, cohort analysis, customer retention, and customer lifetime value.
• Familiarity with LTV modeling and the relationship between customer acquisition costs, retention, and long-term profitability is an advantage.
• Experience working with data warehouses and product analytics platforms.
• Familiarity with payment service provider data, advertising platforms, and marketing analytics is considered a plus.
• Strong business acumen and product intuition, with the ability to connect analytical findings to strategic decisions and measurable business impact.
• Excellent critical thinking skills, including the ability to challenge assumptions, evaluate experimental designs, and question conclusions using evidence.
• Strong communication and presentation abilities, with the capacity to explain statistical results, methodological limitations, and business implications in an accessible way.
• High levels of autonomy, curiosity, initiative, and organizational skills, with the ability to prioritize competing requests across multiple products and teams.
• Comfortable collaborating with multidisciplinary teams and influencing decisions without relying on direct managerial authority.
• Fluency in French is required, as it is the primary working language.
• English proficiency at B2 level or higher is required, as English is the secondary working language and will be assessed during the recruitment process.

Benefits:

• 100% remote working across Europe, with the possibility of working from any European country.
• Flexible working hours, allowing employees to organize their schedules according to their preferences and professional responsibilities.
• Opportunity to work remotely from a different time zone for up to one month.
• Monthly coworking budget of €200 to support employees who prefer working outside their home office.
• Dedicated equipment budget to purchase essential home-office equipment, including laptops, desks, chairs, monitors, and headsets.
• Flexible vacation policy, allowing employees to take the time off they need to recharge and maintain a healthy work-life balance.
• Two company-wide team seminars per year, organized in spring and autumn, offering opportunities for collaboration and team building.
• Professional development opportunities in a rapidly growing business, with the freedom to take ownership of initiatives and shape new analytical practices.
• Opportunity to become the first dedicated Product Data Scientist, with significant autonomy and influence over the development of experimentation and data science capabilities.
• Direct exposure to multiple SaaS products and the entire customer lifecycle, from acquisition to payments and retention.
• Close collaboration with senior leadership, Product Managers, Marketing, Payments, and Data specialists.
• Access to an existing experimentation framework and analytical infrastructure that you can challenge, improve, and develop further.
• For employees based in Lausanne: access to modern office facilities, sports amenities, parking, and financial contributions toward commuting expenses.
• An ambitious, collaborative, and inclusive working environment that values ownership, integrity, performance, innovation, and continuous learning.