Product Data Scientist

Product Data Scientist – Product Analytics & Data Engineering

Location: Fully Remote (West Coast preferred; East Coast considered)

Schedule: Pacific Time business hours preferred

Industry: Financial Technology / Consumer Financial Products

Team Overview

Join a growing Data Science organization supporting consumer-facing financial products across digital banking, lending, payments, and other financial services.

The team focuses on understanding customer behavior, improving product performance, and enabling data-driven decisions across the customer lifecycle. You'll join a collaborative, approximately five-person data science team within a larger organization of roughly 12 professionals, working closely with Product, Engineering, Marketing, and Business stakeholders.

The environment combines established products with new product launches, providing opportunities to own data initiatives from initial instrumentation and pipeline development through dashboarding, reporting, and experimentation.

Position Overview

We are seeking a technically strong Product Data Scientist with advanced SQL, hands-on dashboard development, and product analytics experience.

This role combines product data science with analytics engineering, supporting business metrics, customer behavior analysis, data instrumentation, and experimentation.

The ideal candidate can independently translate business questions into analytical solutions, develop reporting frameworks, and collaborate with cross-functional stakeholders to define and measure product success. The workload is approximately 50% data instrumentation/engineering and 50% analytics, dashboarding, and experimentation, depending on product maturity and business priorities.

Key Responsibilities

• Write advanced SQL queries to extract, transform, model, and analyze large datasets supporting consumer financial products.
• Design, build, and maintain dashboards and reporting solutions using Tableau, Looker, or similar BI platforms.
• Partner directly with Product, Engineering, Marketing, and Business stakeholders to gather requirements, define KPIs, and translate business needs into measurable analytics solutions.
• Analyze customer behavior across onboarding, engagement, conversion, retention, and other stages of the product lifecycle.
• Collaborate with Engineering teams on event instrumentation, data collection, pipeline development, and data validation.
• Develop and maintain data models, reporting datasets, and analytical workflows to support product performance measurement.
• Support A/B testing, experimentation, and statistical analysis to evaluate product changes and business outcomes.
• Identify trends, performance gaps, and opportunities for product optimization through data-driven insights.
• Support both newly launched and established products, adapting analytical approaches based on product maturity.
• Explore AI-assisted analytics and dashboard development tools to improve reporting efficiency and analytical workflows.

Required Qualifications

• Strong hands-on experience with advanced SQL, including complex queries, joins, CTEs, window functions, data transformations, and data modeling.
• Proven experience independently developing dashboards and business intelligence reporting solutions using Tableau, Looker, Power BI, or similar platforms.
• Experience partnering directly with technical and nontechnical stakeholders to gather requirements, define business metrics, and communicate analytical findings.
• Background in product analytics, customer behavior analysis, or business performance measurement, preferably supporting B2C or consumer-facing products.
• Experience working with data instrumentation, event tracking, data validation, ETL processes, or analytical data pipelines.
• Ability to independently manage analytical projects, navigate ambiguous requirements, and deliver actionable insights.
• Strong understanding of data quality, metric definitions, and reporting accuracy.
• Bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.

Preferred Qualifications

• Experience within fintech, banking, lending, payments, or consumer financial services.
• Hands-on experience with BigQuery, Databricks, or similar cloud data platforms.
• Familiarity with dbt, Airflow, or other ETL and data orchestration tools.
• Experience designing, executing, or interpreting A/B tests and product experiments.
• Exposure to statistical analysis, causal inference, or machine learning fundamentals.
• Experience supporting new product launches, including establishing event instrumentation and reporting frameworks.
• Familiarity with AI-assisted development tools such as Claude, Cursor, or similar technologies.
• Experience using Python or R for data analysis and statistical workflows.