Senior Data Scientist
Flinks is an embedded finance platform that provides data connectivity, financial intelligence, document fraud detection, open banking infrastructure, and account-to-account payments. Its customers include financial institutions, lenders, fintechs, and other businesses building financial experiences.
Projects
About Flinks
Flinks provides APIs, dashboards, and managed services for connecting to financial accounts, retrieving and enriching banking data, verifying documents, detecting fraud, initiating EFT and Interac payments, and operating open banking ecosystems. Its products include Connect, Upload, Enrich, Pay, Outbound, and Professional Services. Flinks serves financial institutions, credit unions, lenders, fintechs, and businesses requiring onboarding, KYC/KYB, underwriting, account funding, payment, fraud detection, and financial data infrastructure capabilities.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Own and ship production machine-learning models for transaction categorization, reversal detection, multilingual NER, payments risk, balance forecasting, and financial data enrichment. The role focuses on model quality and lifecycle management on a shared data platform.
Requirements
- 6-8 years building and shipping machine-learning models, including taking models to production.
- Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
- Production-grade Python skills.
- Ability to take a model independently to a live, monitored service.
- Solid data science and machine-learning 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 using 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 risk reduction, enrichment accuracy, customer adoption, operational efficiency, 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
- Career development, learning opportunities and growth