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Data Analyst Payments Analytics - Vice President
senior · Data Science & Analytics
We are seeking a Data Analyst to elevate our analytics capabilities, strengthen core reporting, and help design and operationalize AI-augmented analytics that improve how we understand partner and merchant behavior and outcomes.
As a Data Analyst in SMB Payments , you will play a pivotal role in identifying key metrics, building durable reporting, and conducting comprehensive analyses to inform business strategies. You will work collaboratively with cross-functional teams—including Product, Sales, Marketing, Account Management, and Risk—to drive product upselling and cross-selling, enhancing both partner and merchant experiences.You will also contribute to the team’s LLM/AI analytics roadmap by applying GenAI thoughtfully to unlock insights from unstructured and semi-structured data (e.g., call notes, tickets, feedback, emails where permitted), automate narrative generation, and improve analytic workflows—while ensuring strong controls, data privacy, and measurable quality.
The JPMorganChase SMB Payments Analytics team is dedicated to cultivating a data-driven culture and empowering fact-based decision-making. Our Business Analytics division champions this mission by delivering data, insights, and scalable reporting—while also accelerating the integration of modern analytics techniques, including LLM/GenAI-enabled insights, into everyday decisioning.
Job Responsibilities
- Identify, define, and maintain core SMB Payments performance metrics; build scorecards/dashboards and recurring reporting for stakeholders.
- Perform deep-dive analyses to surface trends, drivers, and root causes; translate findings into clear narratives and recommended actions.
- Build scalable datasets and self-service analytics assets with clear documentation (metric definitions, assumptions, and data lineage).
- Partner with Product and commercial teams to size opportunities, monitor funnel performance, and measure outcomes of upsell/cross-sell initiatives.
- Apply LLMs and modern AI techniques to augment analytics workflows (e.g., summarization, topic modeling/classification, semantic search, insight extraction) and reduce time-to-insight.
- Help design and run LLM evaluation and monitoring (quality, robustness, latency, cost) and contribute to improving prompts, retrieval approaches, and end-to-end performance.
- Use critical thinking and advanced analytics to diagnose underperforming models/pipelines (data issues, drift, prompt failures, weak retrieval, label quality).
Collaborate with partners across Product, Engineering, Data, and Risk to ensure AI-enabled solutions are secure, compliant, auditable, and reliable in production.
Required Qualifications, Capabilities, and Skills
- 5+ years of experience in analytics, business analytics, or data analysis delivering business-critical insights and reporting.
- Fluency in SQL and Python for analysis, automation, and reproducible analytics.
- Strong ability to frame ambiguous business problems, design analyses, and communicate results to both technical and non-technical audiences.
- Experience building dashboards and visualizations (e.g., Tableau; other BI tools acceptable).
- Demonstrated exposure to AI/LLM concepts and applied use cases (prompting/evaluation, embeddings/semantic search, text analytics, or ML experimentation) with a strong interest in expanding hands-on delivery.
- Strong collaboration skills across cross-functional stakeholders (Product, Sales, Marketing, Account Management, Risk).
Preferred Qualifications, Capabilities, and Skills
- Bachelor’s degree (Master’s a plus) in Computer Science, Statistics, Economics, Mathematics, Engineering, or related field (or equivalent experience).
- Knowledge of consumer/retail banking and/or payments products (SMB/payments domain strongly preferred).
- Working knowledge of Alteryx and advanced Tableau development.
- Experience with big data and modern data ecosystems (e.g., Spark, cloud data platforms, distributed querying).
- Experience with ML/AI tooling (e.g., scikit-learn, PyTorch/TensorFlow) and/or deploying analytics into production workflows.