Data Analyst Payments Analytics - Vice President
On-sitePlano, Texas, United States
Job Summary
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, translating findings into clear narratives and recommended actions. Build scalable datasets and self-service analytics assets with clear documentation while applying LLMs to augment workflows, automate narrative generation, and extract insights from unstructured data. Partner with Product, Sales, Marketing, Account Management, and Risk teams to size opportunities, monitor funnel performance, and measure upsell/cross-sell outcomes. Help design and run LLM evaluation and monitoring to ensure AI-enabled solutions are secure, compliant, and reliable in production.
Required Qualifications
- 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).
Desired Qualifications
- 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.
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