Staff ML Scientist / Engineer

We’re looking for a strong technical contributor with a background in machine learning and systems engineering. Bonus point if they owned the roadmap of services/tools used by more than one internal team. This is a highly autonomous person that identifies low-hanging fruit worth tackling to move us ever closer to the vision they create for the problem-space they own. They must be able to communicate with stakeholders and users, steer short-term deliveries without compromising milestones in the roadmap, set guidelines and standards.

They collaborate with Data Science Leads to align work that would otherwise be siloed. They review experiments and design documents to improve projects from the get-go. 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. About

the Role

Our team exists to enable hundreds of specialized risk ML models – their lifecycle, governance and interface with compliance – while removing as much of the maintenance toil from Data Scientist and product teams. We split the work in two tracks: Labels and Models

• Labels: central platform for risk labels
• Models: libraries and services to experiment, eval, promote and track models

Historically Wise is a company of very autonomous and distributed teams, which means different implementations exist to prevent and mitigate risk vectors. We’ve invested a lot into centralizing risk assessments and their inputs (features) and have strong adoption from FinCrime, Verification, Mitigation and Global Products teams. We’re now expanding this investment to the next critical component in scaling Wise: more and better ML /AI while keeping auditors and regulators happy.

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: drive experiments to prove value of new methodologies / algorithms applied to cross-team domains

You will drive high-priority projects from proof-of-concept to MVP, to service / tooling, estimate and measure impact.