Senior Data Scientist
Flinks provides financial data connectivity, intelligence, fraud detection, open banking infrastructure, and account-to-account payment solutions. Its customers include financial institutions, lenders, fintechs, and other businesses building financial experiences.
Projects
About Flinks
Flinks is a financial technology company that combines connectivity, intelligence, and payments infrastructure. Its products include Connect for linking bank accounts and accessing financial data, Upload for document authentication and fraud detection, Enrich for transforming raw financial data into decisioning insights, Pay for account-to-account payments, and Outbound for managed open banking infrastructure. Flinks serves financial institutions, consumer and business lenders, fintechs, and other organizations that need onboarding, income verification, underwriting, fraud prevention, financial analysis, data sharing, and payment capabilities.
Skills
About the Role
You will own machine-learning models end to end, from framing the problem and writing the design/RFC through building the training pipeline, deploying to a live endpoint, and monitoring model quality in production. You will build and ship your own models on a shared platform operated by Data Engineering, watch drift and performance, and decide when to retrain. You will evaluate rigorously using experimental design, statistical validation, drift detection, and champion-challenger promotion. You will work on live ML systems such as transaction categorization with a multi-task BERT classifier, reversal detection, a multilingual transaction NER parser, payments risk and balance forecasting, and an enrichment suite covering income, frequency, and life-event models. You will collaborate with Data Engineering, backend, product, and QA on contracts, deployment, and rollout, and use AI-assisted development to accelerate implementation and experimentation.
Requirements
- 6-8 years building and shipping machine-learning models, including taking models to production
- Bachelor's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, or related); Master's or PhD is an asset
- Production-grade Python skills
- Ability to take a model to a live, monitored service independently
- Solid data science and ML foundation
- Legally authorized to work in Canada
Responsibilities
- Own ML models end to end, from problem framing and design/RFC through training, deployment, and production monitoring
- Build model training pipelines and package models for serving on the shared platform
- Monitor model drift and performance and decide when to retrain
- Design rigorous evaluations including experimental design, statistical validation, drift detection, and champion-challenger promotion
- Collaborate with Data Engineering, backend, product, and QA on contracts, deployment, and rollout
- Connect model improvements to business outcomes such as risk reduction, enrichment accuracy, customer adoption, and revenue
- Use AI-assisted development to accelerate implementation and experimentation
Benefits
- Health & Dental coverage as of Day 1
- Flexible Paid Time Off (FTO)
- Remote work environment with frequent in-person gatherings and activities
