Senior Data Scientist

About the role

About Flinks Flinks is the embedded finance platform that brings together connectivity, intelligence, and payments - giving businesses the infrastructure they need to build and deliver seamless financial experiences at scale. As a leader in Open Finance in Canada, we've grown since 2016 into one of North America's most trusted platforms for financial data access, enrichment, and money movement. We work with innovators across many industries, including lending, fintech, banking, insurance, and wealth management. Today, our platform connects to 15,000+ financial institutions across North America and powers over 1M monthly connections. We also give our customers unprecedented visibility into 4,500+ real-time financial insights to support smarter decisioning. Companies rely on Flinks to streamline onboarding, verify income, assess credit risk, and power faster payment experiences. We're on a mission to drive financial innovation and help businesses build financial experiences that feel effortless, connected, and customer-first. That's where you come in. The Role We're hiring a Senior Data Scientist to own machine-learning models end to end - from framing the problem and designing the model through the training pipeline, deployment to a live endpoint and the model-quality monitoring that keeps it accurate in production. This is a hands-on, engineering-heavy data-science role: you build and ship your own models. Where this role sits: you own the models and their quality. Our Data Engineering function owns the shared data platform and serving infrastructure you build on - the warehouse, pipelines, governance, CI/CD and the operational reliability of the serving endpoints - so you stay focused on the science and the model lifecycle, not on running the platform. We're explicit about this because it matters: if you're looking for a pure research or notebook-only role, this isn't it. If you're energized by taking a model all the way to a live, monitored endpoint and seeing it drive real financial decisions - you'll thrive here. We're not building ML for ML's sake : we judge models by the business outcomes they move - risk reduction, enrichment accuracy, customer adoption, operational efficiency, revenue and connecting your model improvements to those outcomes is part of the role. What You'll Do Own ML models end to end - frame the problem, write the design/RFC, build and train the model, ship it to a served endpoint and monitor its quality in production. Build your model's training pipeline and package it for serving, deploying onto the shared platform Data Engineering builds and operates. Own model quality, not the platform - you watch drift and performance and decide when to retrain. Evaluate rigorously - experimental design, statistical validation, drift detection and retraining, champion-challenger evaluation and promotion. Partner across the org - your model outputs feed the attributes/enrichment layer, payments risk, dashboards and client integrations, you'll collaborate with Data Engineering, backend, product and QA on contracts, deployment and rollout. Move fast with AI-assisted development -we use it to accelerate implementation and experimentation, the highest-leverage contribution in this role comes from strong problem framing, system design, evaluation rigor and clear technical specifications. What You'll Work On A few of the live ML systems this role owns end to end: Transaction categorization - a hierarchical, multi-task BERT classifier running as segment based models, trained on labelled data with synthetic top-ups for rare classes. Reversal / "final-category" detection - a hybrid ML + analytical-rule model classifying reversal types and linking each back to its original debit, promoted via champion-challenger . Transaction NER parser - a multilingual token-classifier extracting entity types from raw descriptions. Payments risk and balance forecasting - quantile time-series forecasting feeding a risk/offer decision layer. The enrichment suite - income / net-income, frequency detection, plus employment-loss, life-events and pay-frequency models. Model-quality and analytics tooling - the data-science team's own model-performance, PSI / drift and category-distribution monitoring for the models it owns. Our stack Python and SQL Google Cloud Platform BigQuery and modern data tooling PyTorch, HuggingFace and classical ML frameworks MLflow and Kubeflow FastAPI and containerized deployment Azure DevOps You don't need experience with every tool listed above - strong production-ML fundamentals matter more than direct experience with our exact stack. Python is the exception: it's a non-negotiable (see Key Requirements). Why This Role High ownership, low bureaucracy - you own models end to end and watch them drive real financial decisions for banks and fintechs. A modern, cloud-native ML stack with an AI-assisted development workflow. Real scale and real stakes - regulated financial data, enrichment and payments. K

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