Staff ML Scientist / Engineer

Company 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 and what we offer .

Job Description

• 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.

Qualifications

A bit about you:

• Extensive experience with end-to-end distributed data systems, specially ML-centric ones;
• Previous experience as Data/ML Scientist/Engineer in large scale product;
• Excellent Software Engineering skills. Ability to work with Python and Java;
• Strong drive to solve problems for Data Scientists, high agency in complex scenarios;
• Ability to build incrementally, think from first principles and prioritize simpler solutions;
• Good communication skills with technical and non-technical audiences, 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, GraphFrames, Ray
• Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML
• AWS (S3, EMR, SageMaker, Lakeformation)
• Terraform, Docker, GitHub CI/CD
• A/B testing. ML explainability

Additional Information

Interested? Find out more:

• How we work – a practical guide
• DEI @ Wise
• Our Data Science career map

What do we offer:

• Wise's Restricted Stock Units (RSU's).
• Benefits

at Wise and our offices

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

• We're proud to have a truly international team, and we celebrate our differences.
• Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
• If you want to find out more about what it's like to work at Wise visit Wise.Jobs .
• Keep up to date with life at Wise by following us on LinkedIn and Instagram .