Lead ML Engineer

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 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 hundreds of models

• Data Engineering: distributed processing at terabyte scale

• Science: prove value of new methodologies / algorithms applied to cross-team domains, estimate and measure impact, mentor junior members in experiment design

Qualifications

A bit about you:

• Extensive experience with end-to-end distributed data systems, specially ML-centric ones;

• Previous experience as Data Scientist in large scale product team / business;

• Excellent Python and Software Engineering knowledge. Ability to work with Java if needed. Demonstrable experience collaborating with engineers on services.;

• Strong drive to solve problems for Data Scientists, with the ability to work independently in a cross-functional and cross-team environment;

• Good communication skills, ability to get the point across to non-technical individuals and back it up with data (and statistical analysis), to engage and manage project stakeholders;

• Strong problem solving skills with the ability to help refine problem statements and propose solutions taking effort-impact-scalability tradeoff into account.

Some skills that will make you stand out:

• 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