Product Data Scientist (SaaS)

Location: Remote, candidates based in Europe

Company

Technology company operating multiple subscription-based SaaS products for customers worldwide, managing acquisition, conversion, payments, product usage, and retention.

What you will do

• Design and continuously improve the A/B testing framework, including hypotheses, metrics, statistical power, test duration, and decision criteria.
• Review experimentation processes, improve test reliability, and introduce practical methods, tools, and best practices.
• Partner with Product, Marketing, Development, Payments, Data Engineering, and Data Analytics teams to design experiments, validate hypotheses, and interpret results.
• Analyze acquisition, conversion, product usage, payments, and retention journeys through funnel and cohort analysis to identify optimization opportunities.
• Evaluate LTV, CAC, payback periods, transaction performance, customer value signals, and advertising effectiveness across products, channels, and segments.
• Ensure data quality and consistent metric definitions, translate business questions into measurable experiments, and communicate findings and limitations to technical and non-technical stakeholders.

Requirements

• 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 and translating findings into product or business improvements.
• Strong knowledge of statistics, statistical inference, hypothesis testing, power analysis, multiple testing, and experiment design.
• Advanced Python and SQL skills for data analysis, statistical modeling, and experimentation.
• Experience with high-traffic products or large datasets, ideally in SaaS, subscription services, or e-commerce.
• Understanding of funnel analysis, cohort analysis, retention metrics, and customer value; experience with data warehouses and product analytics tools.
• Strong business acumen, product thinking, critical thinking, communication skills, and the ability to 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 LTV modeling, CAC, payback analysis, marketing analytics, advertising platforms, and acquisition attribution.
• Experience analyzing payment performance, transaction data, or PSP integrations.
• Knowledge of advertising value signals and their use in campaign optimization.
• Experience improving experimentation frameworks or introducing standardized A/B testing practices.

Culture & Benefits

• Remote work with a flexible working schedule.
• Up to 20 working days of vacation, 10 working days of sick leave, and up to 11 national holidays.
• Medical reimbursement or insurance after 3 months.
• Online English courses, online and offline team-building activities, and support for a volunteering culture.
• Direct cooperation with the customer.