Product Data Scientist

About the role

Solvd, an AI-native consulting and technology services firm, is hiring a Product Data Scientist to partner with its Credit Cards and Banking product teams. The role shapes product strategy and drives growth through analysis, experimentation and applied machine learning, working alongside Product Managers, engineers and business stakeholders.

Key responsibilities

• Partner with Product Managers and vertical leadership to identify, size and prioritize growth opportunities across the consumer journey.
• Establish metric frameworks, including north-star metrics, funnel definitions and performance drivers.
• Lead deep-dive analyses of consumer behavior, friction points and performance trends, turning ambiguous questions into actionable recommendations.
• Own the full experimentation lifecycle: hypothesis development, test design, statistical analysis and business impact measurement.
• Develop predictive and statistical models that improve consumer experiences and help translate findings into features.
• Build and maintain metric definitions, dashboards and measurement frameworks, automate recurring analyses, and communicate findings to technical and non-technical stakeholders.

Required profile

• 3+ years of professional experience in data science, product analytics or a related field, with demonstrated influence on product decisions.
• Bachelor's or advanced degree in Statistics, Mathematics, Economics, Computer Science, Engineering or a related quantitative discipline, or equivalent practical experience.
• Excellent communication and collaboration skills, able to influence Product Managers, Engineers and business stakeholders.

Required skills

• Strong proficiency in SQL and Python.
• Hands-on experience with Databricks (required).
• Proven experience designing and analyzing A/B tests, including statistical significance and power analysis.
• Understanding of product analytics: funnel optimization, metric development, cohort analysis and behavioral segmentation.
• Working knowledge of applied statistics and machine learning (regression, classification, propensity modeling, causal inference).
• Experience building dashboards and automating workflows with BI tools.