Join our client’s growing Data team as a Product Data Scientist and help turn complex product data into actionable insights, strategic business decisions, and measurable growth across multiple SaaS products.
About the project
Our client is a Switzerland-based technology company operating multiple subscription-based SaaS products for customers worldwide. The company manages the entire customer journey — from acquisition and conversion to payments, product usage, and retention — with a strong focus on data-driven growth and continuous optimization.
You will join a growing Data department, working alongside a Data Engineer and a Data Analyst and reporting directly to the Chief Data Officer. The role involves close collaboration with the CEO, Product Managers, and Marketing, Payments, and Development teams.
Your impact
You will play a key role in shaping data-driven decisions and improving experimentation practices across multiple SaaS products. By analyzing customer behavior, optimizing A/B testing strategies, and identifying growth opportunities, you will help teams enhance product performance, increase customer value, and drive measurable business results.
What makes you a great fit
- 3–5 years of experience in Product Data Science, Growth Analytics, or a similar analytical role
- Hands-on experience designing, running, and analyzing A/B tests, with a proven ability to translate findings into product or business improvements
- Strong knowledge of statistics, statistical inference, hypothesis testing, power analysis, multiple testing, and experiment design
- Advanced SQL and Python skills for data analysis, statistical modeling, and experimentation
- Experience working with high-traffic products or large datasets, ideally in SaaS, subscription-based services, or e-commerce
- Solid understanding of funnel analysis, cohort analysis, retention metrics, and customer value
- Experience working with data warehouses and product analytics tools
- Strong business acumen and product thinking, with the ability to connect analytical findings to measurable business outcomes
- Critical thinking, excellent communication skills, and the confidence to challenge assumptions and explain complex findings to non-technical stakeholders
- Ability to work independently, prioritize effectively, and collaborate across multiple teams
- Fluent French and at least B2-level English
Nice to have
- Experience with customer lifetime value (LTV) modeling, customer acquisition cost (CAC), and payback analysis
- Familiarity with marketing analytics, advertising platforms, and acquisition attribution
- Experience analyzing payment performance, transaction data, or payment service provider (PSP) integrations
- Knowledge of advertising value signals and their use in campaign optimization
- Previous experience improving experimentation frameworks or introducing standardized A/B testing practices
What you will do
- Design, implement, and continuously improve a robust A/B testing framework, covering hypothesis formulation, metric selection, statistical power, test duration, and decision-making criteria
- Review existing experimentation processes, identify weaknesses, and introduce practical improvements to increase the reliability of test results
- Partner with Product, Marketing, and Development teams to design experiments, validate hypotheses, and interpret results
- Analyze customer journeys across acquisition, conversion, product usage, payments, and retention to identify opportunities for optimization
- Conduct funnel and cohort analyses to uncover friction points, understand user behavior, and recommend actionable improvements
- Evaluate customer lifetime value, acquisition costs, and payback periods across different products, channels, and customer segments
- Collaborate with the Payments team to analyze transaction performance, identify risks, and evaluate optimization opportunities
- Work closely with the Data Engineer to improve the use of customer value signals in advertising platforms and measure their effectiveness
- Partner with the Data Analyst and Data Engineer to ensure data quality, consistent metric definitions, and reliable analytical insights
- Translate complex business questions into structured analytical approaches and measurable experiments
- Present findings, statistical limitations, and recommendations clearly to both technical and non-technical stakeholders
- Help teams develop greater independence in experimentation by introducing practical methods, tools, and best practices
Perks & Benefits
- Vacation (up to 20 working days)
- Sick leave (10 working days)
- Up to 11 national holidays as a time off
- Medical reimbursement or insurance after 3 months
- Online English courses
- Flexible working schedule and remote work
- Direct cooperation with the customer
- Online and offline teambuildings
- Support for and development of our volunteering culture
We welcome applicants of every background, identity, and life experience. If you’re excited about this role but don’t check every box, we’d still like to hear from you. Sounds like a match? Apply now and let’s build something great together. Send CV
