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.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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