What we are looking for

The Senior Analyst: Payments Data Intelligence (PDI) serves as the senior technical lead and quantitative subject matter expert responsible for the hands-on design, execution, and validation of PDI’s analytics engine and predictive modeling workflows. Positioned strategically between analytical support and the PDI Manager, this role owns the technical and statistical integrity of multi-rail payment datasets.

The Senior Analyst operates with full technical autonomy over codebases, complex SQL transformations, Python feature engineering, GCP data pipelines, and MLOps back testing suites. This role transforms high-level business queries and scenario specifications into production-ready analytical pipelines, automated data quality controls, and validated quantitative outputs. Acting as a genuine analytical thought-partner who understands the statistical significance and underlying data structures behind feature choices, this role ensures data readiness, maintains the analytics environment, validates model inputs, and supports repeatable workflows across multi-rail payment data (Lynx, ACSS, RTR, and other external payment rails). This individual is a self-directed, highly curious professional capable of bridging technical execution with strategic product goals, working cross-functionally with Data Engineering and Product teams to transform multi-source payment data into trusted, production-ready assets.

Technical Data Ingestion Automated Quality Control (Data Layer Ownership)

Own the technical design and execution of complex SQL/Python scripts to ingest, transform, and normalize multi-rail transaction data and exogenous feeds.

Design and maintain automated data validation pipelines, schema enforcement, anomaly flags,



and reliability score tagging prior to modeling ingestion.

Build and optimize clean, scalable, and documented multi-rail data structures within Google Cloud Platform (BigQuery) to support ongoing cross-rail intelligence.

Data Preparation and Modelling Readiness

Assemble and prepare multi-rail payment data (Lynx, ACSS, RTR) and relevant external/exogenous datasets so they are structured for time-series and modelling work.

Perform data cleansing, normalization, and reconciliation to ensure model inputs are consistent, accurate, and trustworthy.

Build repeatable, automated checks for model input quality, completeness, consistency, and anomaly flags so issues are identified prior to modelling.

Establish clean, well-documented, repeatable workflows for bringing new internal and external data sources into the PDI modelling pipeline.

Data Analytics

Develop an understanding of the payments business to produce business insights, discover patterns and interpret trends stemming from data analysis.

Lead hands-on time-series decomposition, cross-rail migration analytics, correlation mapping, and advanced feature engineering.

Develop and code reproducible analytical pipelines that model volume, value, structural shifts, and payment velocity across rails.

Prepare datasets and analytical outputs for back-testing, model performance evaluation,



and stress/scenario analysis.

Code reproducible analytical pipelines that model volume, value, structural shifts, and payment velocity across rails.

Produce analytical judgment on data structure and feature choices, providing a critical second set of eyes on the data side of modelling decisions.

Conduct the integration of model outputs (such as explainability/attribution views and pricing-related outputs) into downstream business tools and executive reporting.

Develop and maintain clear and concise documentation on process, data sources, and models development

Setting and Technical Workflow

Manage day-to-day analytics environment configuration (Big Query, Cloud Storage, Vertex AI tooling and related tools), query performance, and computational efficiency escalating complex security or platform work appropriately.

Maintain the technical workflow that transitions models into production, including basic automation for repeatable deployment steps.

Ensure and maintain reliable input-quality checks and basic performance monitoring for production models so performance issues are caught early.

Facilitate the integration of model outputs (e.g., explainability, attribution, pricing outputs) into downstream business tools.

Establish and enforce SQL/Python coding standards, documentation practices, and reproducible analytical workflows.

Author technical data documentation, model validation summaries, and methodology briefs supporting Stage Gate reviews.

Cross-Functional Alignment and Stakeholder Delivery

Work day-to-day in close partnership with the Lead Data Scientist to prioritize analytical and prepara

📌 Senior Analyst, Payments Data Intelligence (Montreal)
🏢 Sweeping Corporation of America
📍 Montreal