Senior Product Analyst (Machine Learning & Analytics)

Quick Facts

• Role: Senior Product Analyst (Machine Learning and Analytics)
• Focus: Product and financial analytics, experimentation, and data-driven process development

Description

You will conduct strategic analyses to set directions for financial products and assess their impact on organizational financial results. You’ll calculate key financial metrics (e.g., incrementality and CLV) with full auditability and reliability, and build data processes that follow best practices. You’ll collaborate with data scientists, risk specialists, and software engineers to develop recommendations that influence customer behavior on the platform.

Responsibilities

• Contribute to product strategy and direction for financial services offered on Allegro
• Recommend actions to key decision-makers based on analytical findings
• Compute major financial metrics (e.g., incrementality of financial products, CLV) with auditable data pipelines
• Create and maintain data processes with software engineering best practices
• Participate in the design and production implementation of data-driven business processes

Requirements

• Minimum 5 years of experience in product/data analytics or data engineering (consulting or financial institution experience welcome)
• Fluent SQL, including complex query writing and SQL optimization
• Experience creating dashboards in BI tools (Tableau preferred)
• Care for repeatability and reusability of code (Git knowledge advantageous)
• Experience with Python in data processing and analytics
• Ability to transform data into knowledge, draw conclusions, and provide insightful recommendations
• Knowledge of experimental design, A/B testing methodologies, and product analytics metrics
• Independent, proactive, inquisitive, and meticulous
• Strong team collaboration and willingness to take on responsibilities
• Polish and English at least B2 level

Benefits

• Well-equipped offices and strong work tools (e.g., ergonomic equipment, interactive conference rooms)
• Cafeteria-style fringe benefits (medical, sports/lunch packages, insurance, vouchers)
• Paid English classes related to the job
• Training budget and learning platform, plus hackathons and internal events
• Additional volunteering day off
• Social events and community activities