Senior Data Scientist

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Flinks

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

💳 Fintech

🏦 Banking

💸 Finance

Fintech • Banking • Finance

Flinks is a company that provides financial data infrastructure to power innovation in the financial sector. It enables users to safely connect their bank accounts and transform raw transaction data into actionable insights, supporting open banking programs. Its solutions optimize loan underwriting, enhance digital banking, facilitate payments, and enable wealth management. By leveraging its extensive network of third-party applications, Flinks helps financial institutions innovate and build the financial systems of the future, serving the needs of fintech, wealth management, and traditional banking industries.

📋 Description

• 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.

🎯 Requirements

• Experience: 6-8 years building and shipping machine-learning models, including taking models to production yourself (training pipeline → served endpoint → monitoring). • Education: Bachelor's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, or related), a Master's or PhD is an asset, not a requirement. • Non-negotiables: production-grade Python and the ability to take a model to a live, monitored service yourself, on a solid data-science / ML foundation.

🏖️ 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 • And more