Product Data Scientist / Data Operations Analyst - W2 80-88/hr

Key Responsibilities

• Product Analytics & Strategy: Analyze customer behavior across onboarding, engagement, and retention journeys to surface actionable insights for product and business teams. Translate complex data into clear metrics that inform executive decision-making.
• Experimentation & A/B Testing: Design, execute, and analyze standard and multi-arm A/B experiments. Perform lightweight causal inference analyses (e.g., Propensity Score Matching, Difference-in-Differences) to evaluate feature impacts.
• Data Pipelines & Instrumentation: Partner closely with engineering teams to instrument event tracking across new digital product screens. Build, check, and maintain robust data pipelines to ensure seamless data flow.
• BI & AI-Native Dashboarding: Develop traditional business intelligence dashboards (Tableau, Looker) and utilize AI-native workflows (Claude, Cursor, GitHub) to accelerate analytics readouts and reporting.
• Cross-Functional Collaboration: Communicate findings clearly to Product, Marketing, Engineering, and Design partners, acting as an embedded data advisor without requiring hand-holding.

Technical Stack & Environment

• Core Querying & Modeling: Advanced SQL (complex joins, CTEs, window functions, query optimization) and Data Modeling.
• Data Warehouses & Frameworks: BigQuery, Databricks, and cloud data platforms.
• ETL & Data Engineering Tools: dbt, Apache Airflow, ETL pipelines, and event instrumentation frameworks.
• Visualization & Analytics Tools: Tableau, Looker, GitHub dashboards, and AI-native code generators (Claude, Cursor).
• Experimentation Methodologies: A/B Testing, A/B/n testing, Hypothesis Testing, Causal Inference (PSM, DiD).

Required Qualifications

• Experience: 4 6 years of experience in Data Science, Product Analytics, or Analytics Engineering within a B2C digital product setting.
• SQL Mastery: Exceptional, expert-level SQL skills with the ability to live-code complex queries.
• Experimentation Expertise: Hands-on experience designing, running, and interpreting standard A/B test readouts.
• Pipeline Exposure: Solid foundation in data pipeline architecture, data validation, and ETL workflows.
• Business Acumen: Deep appreciation for product customer journeys, growth metrics, and cross-functional communication with non-technical stakeholders.
• Education: Bachelor s or Master s degree in Statistics, Mathematics, Computer Science, Operations Research, Econometrics, Engineering, or a related quantitative field.

Nice-to-Have Experience

• Experience in Fintech, Digital Banking, Payments, or Lending product analytics.
• Exposure to workflow orchestration (Airflow) and data transformation frameworks (dbt).

Keywords

Product Data Scientist, Data Operations Analyst, Senior Data Analyst, Product Analytics, SQL, Data Modeling, Experimentation, A/B Testing, Causal Inference, Propensity Score Matching, Difference-in-Differences, Databricks, BigQuery, dbt, Apache Airflow, ETL, Data Pipelines, Data Instrumentation, Tableau, Looker, BI Dashboards, AI Native, Claude, Cursor, GitHub, B2C Product, Customer Retention, Onboarding Analytics, Metrics Definition, Data Science