Product Data Scientist SaaS

Jobgether; España; Remote; EUR 60,000 - 90,000; Full time

Job summary

Jobgether, a partner company, seeks an experienced Product Data Scientist to drive data-informed decision making across international SaaS products. You will transform complex datasets into insights, shape experiments, and influence product and marketing strategies while working in a fully remote, globally distributed team.

You will lead A/B testing practices, design robust experimental plans, analyze results, and communicate uncertainties clearly to stakeholders.

Qualifications

• 3–5 years in Product Data Science / Growth Data Science or similar.
• Experience with high-traffic digital products or large datasets, preferably SaaS/e-commerce.
• Hands-on in experimental design, analysis, and improving product performance via data.
• Strong statistics and inferential knowledge; choose methods by business questions and data.
• Proficiency in SQL and Python for data analysis, modeling, and experimentation.
• Understanding of product analytics: funnels, cohorts, retention, and LTV.
• Experience with data warehouses and product analytics platforms.
• Knowledge of payment platform data and marketing analytics is a plus.
• Excellent communication, with ability to explain results to technical and non-technical stakeholders.
• Autonomy, curiosity, initiative, and ability to prioritize across multiple products.

Responsibilities

• Review and challenge existing A/B testing processes and recommend improvements.
• Develop and implement a robust experimentation framework with clear criteria for decisions.
• Design, review, and validate experiment plans with Product and business teams before launch.
• Analyze completed experiments, interpret results, and recommend next steps.
• Improve experimentation methodologies and analytical tools for broad team adoption.
• Analyze data across customer journey to identify growth opportunities and revenue drivers.
• Investigate conversion funnels and user behavior to develop growth hypotheses and experiments.
• Collaborate with Payments to analyze transaction performance and improve payment success.
• Evaluate customer value across cohorts, channels, products, and segments.
• Improve LTV, CAC, and payback analyses to inform marketing investments.
• Work with Data Engineers to improve data reliability and definitions of metrics.
• Translate business questions into analytical approaches and testing protocols.
• Present findings and uncertainties clearly to stakeholders.

Skills

Advanced SQL

Python for data analysis

A/B testing design

Experimentation framework

Product analytics

LTV modeling

Cohort analysis

Data warehouses

Communication

Autonomy

Stakeholder influence

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Product Data Scientist SaaS based in Spain. We are seeking an experienced Product Data Scientist to drive data-informed decision-making and experimentation across multiple international SaaS products. In this role, you will transform complex datasets into actionable insights, helping teams understand customer behavior, optimize product performance, and accelerate business growth. You will take a leading role in strengthening A/B testing practices, designing robust experiments, and measuring the real impact of product and marketing initiatives. Working closely with Data, Product, Marketing, Payments, and executive teams, you will influence strategic decisions throughout the entire customer journey. You will also explore customer lifetime value, acquisition efficiency, payment performance, and retention opportunities. This is a hands‑on, business‑oriented opportunity to build and shape product data science practices in a fast‑growing, fully remote international environment.

Responsibilities

• 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.