Product Data Scientist

Role overview Partner with credit card and banking product teams to use data science and experimentation to shape product decisions and support growth. The work spans consumer journey analysis, predictive modeling and measurement, with findings translated into product changes alongside product managers, engineers and business stakeholders.

Responsibilities - Identify, size and prioritize growth opportunities across the consumer journey with product and business leaders. - Define product metrics, funnel measures and performance drivers, then investigate user behavior and friction points. - Turn open-ended business questions into analytical plans and recommendations. - Lead experiments from hypothesis and test design through statistical analysis and impact measurement. - Build predictive or statistical models and work with product and engineering teams to apply them in consumer-facing features. - Maintain dashboards and measurement frameworks, automate recurring analysis, and communicate findings to varied audiences.

Requirements - At least 3 years in data science, product analytics or a related analytical field, with evidence of influencing product decisions. - Strong SQL and Python skills for exploration, statistics, automation and predictive modeling. - Hands-on experience with Databricks for analysis, data processing or model development. - Experience designing and interpreting A/B tests, including statistical significance, power analysis and common pitfalls. - Understanding of funnels, metric development, cohorts and behavioral segmentation. - Working knowledge of applied statistics and machine learning, such as regression, classification, propensity modeling or causal inference. - Experience with dashboards and analytical workflow automation, plus clear communication with technical and non-technical partners. - A quantitative degree or equivalent practical experience.

Benefits and work setup The role offers collaboration across a global team and opportunities to contribute to applied AI work, with an emphasis on continuous learning, innovation and responsible AI.