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# machine learning engineer in fintech

- **Company:** Описаниеwise
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Amazon S3, Continuous Integration, Disaster Recovery, Distributed Data Store, Github, Graph Database, Python (Programming Language), Machine Learning, Software Engineering, Pytorch, Retrieval-Augmented Generation, Apache Spark, Scikit Learn, Xgboost, Apache Kafka, Machine Learning Operations, Terraform, Data Pipelines, Docker
- **Published:** October 1, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/machine-learning-engineer-in-fintech/49545087

## About the Role

measure impact;Mentor junior members in experiment design.ТребованияExtensive 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.УсловияNo conditions specified #J-18808-Ljbffr

## Description

ОписаниеWise 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.ЗадачиOwn 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