Wise
FinTech
LeadMLEngineer/Scientist
Neural analysis suggests this role is
optimal for mid candidates.
“Lead ML Engineer / Scientist at Wise. Skills: Machine Learning, Data Engineering, MLOps, Software Engineering. Remove bottlenecks from Data Science workflows. Provide ML tooling for experiments”
Industry & Context.
Problem solving; Refine problem statements; Propose solutions; Effort-impact-scalability tradeoff analysis
What They're Looking For.
Must Have
Extensive experience with end-to-end distributed data systems, ML-centric experience, Previous experience as Data Scientist, Excellent Python knowledge, Excellent Software Engineering knowledge, Ability to work with Java, Demonstrable experience collaborating with engineers, Drive to solve problems for Data Scientists, Ability to work independently, Good communication skills, Ability to get point across to non-technical individuals, Ability to back up with data, Ability to engage and manage project, Problem solving skills, Ability to help refine problem statements, Ability to propose solutions, Consider effort-impact-scalability tradeoff
Nice to Have
Apache Spark experience, Iceberg experience, Kafka experience, Dbt experience, Scikit-Learn experience, XGBoost experience, PyTorch experience, MLFlow experience, GraphFrames experience, Ray experience, AWS experience, Terraform experience, Docker experience, GitHub CI/CD experience, Knowledge Graphs experience, RAG experience, Graph ML experience, Probabilistic programming experience, A/B testing experience
What You'll Do.
Remove bottlenecks from Data Science workflows
Provide ML tooling for experiments
Develop Wise's ML Label Platform
Drive high priority projects from proof-of-concept to MVP
Drive service / tooling development
Own evolution of ML experimentation tooling
Own evolution of label quality
Co-own stakeholder management
Conduct presentations
Maintain good documentation
Maintain progress updates
Drive impactful proof-of-concepts
Bridge gap for two or more teams
Perform software engineering
Perform Data Engineering
Perform Science tasks
Prove value of new methodologies
Prove value of new algorithms
Mentor junior members
How You'll Work.
Team & Collaboration
Cross-functional teams; Cross-team collaboration
Communication Scope
Presentations; Demos; Workshops; Documentation; Progress updates
Process & Methodology
Roadmap planning
Full Job Description
Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere. More about [our mission](https://wise.jobs/our-mission) and [what we offer](https://wise.jobs/what-we-offer). We’re looking for a Lead Machine Learning Engineer to join our growing Servicing Machine Learning and Data Engineering Team in London. This role is a unique opportunity to scale and advance the impact of Data Science in Servicing tribe – namely Fincrime, KYC and Customer Support squads. What you build will have a direct impact on [Wise’s mission](https://www.transferwise.jobs/what-we-do/) and millions of our customers. Our team is responsible for 1) removing bottlenecks from Data Science workflows, 2) providing ML tooling for experiments, 3) developing Wise’s ML Label Platform. Moreover, we are responsible for driving high priority projects from proof-of-concept to MVP, to service / tooling. We are looking for someone to own the evolution of ML experimentation tooling and label quality – at first for Fincrime teams, then for other squads in Servicing. You will co-own stakeholder management, roadmap, delivery and onboarding. You’re also expected to conduct presentations, demos and workshops, in addition to maintaining good documentation and progress updates for your projects. Additionally, you will have the freedom to drive impactful proof-of-concepts of new methodologies and tooling that bridge a gap for two or more teams in Servicing tribe. Here’s how you’ll be contributing: * Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery * MLOps: Terraform and AWS infra, ML governance for
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