machine learning engineer in fintech

Wise is a global technology company building a way to move and manage money worldwide. It enables international transfers, spending abroad, and international payments for people and businesses.

• Own the evolution of ML experimentation tooling and label quality, initially for Fincrime teams and later for other Servicing squads;
• Co-own stakeholder management, roadmap, delivery, and onboarding;
• Conduct presentations, demos, and workshops;
• Maintain documentation and project progress updates;
• Drive impactful proof-of-concepts for methodologies and tooling that bridge gaps across multiple Servicing teams;
• Implement software engineering practices including testing, CI/CD, monitoring, alerting, and disaster recovery;
• Develop MLOps capabilities with Terraform and AWS infrastructure;
• Support ML governance for hundreds of models;
• Build data engineering solutions for distributed processing at terabyte scale;
• Prove the value of new methodologies and algorithms across cross-team domains;
• Estimate and measure impact;
• Mentor junior members in experiment design.

• Extensive experience with end-to-end distributed data systems, especially ML-centric systems;
• Previous experience as a Data Scientist in a large-scale product team or business;
• Excellent Python and Software Engineering knowledge;
• Ability to work with Java when needed;
• Demonstrable experience collaborating with engineers on services;
• Ability to solve problems for Data Scientists independently in cross-functional and cross-team environments;
• Good communication skills and ability to explain ideas to non-technical individuals using data and statistical analysis;
• Ability to engage and manage project stakeholders;
• Strong problem-solving skills;
• Ability to refine problem statements and propose solutions considering effort, impact, and scalability trade-offs;
• Nice to have: Apache Spark, Iceberg, Kafka, dbt, Scikit-Learn, XGBoost, PyTorch, MLFlow, GraphFrames, Ray, AWS S3, EMR, SageMaker, Lakeformation, Terraform, Docker, GitHub CI/CD, Knowledge Graphs, RAG, graph ML, probabilistic programming, A/B testing.

• No conditions specified