Product Data Scientist / Data Operations Analyst
We are seeking a proactive and analytical Product Data Scientist / Data Operations Analyst to join our data science team. This role focuses on driving B2C product growth, optimization, and customer lifecycle insights across core product verticals. You will work at the intersection of data engineering, experimentation, and business metrics, splitting your focus between data pipeline instrumentation and analytical problem-solving.
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
