Product Data Scientist SaaS

Accountabilities:

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.