Wise is hiring a Senior Machine Learning Engineer/Scientist to join the Servicing Machine Learning and Data Engineering Team. You will focus on scaling and advancing the impact of Data Science within the Servicing tribe, supporting Fincrime, KYC, and Customer Support squads, with the freedom to drive impactful cross-team proof-of-concepts.
What You'll Do
- Own the evolution of ML experimentation tooling and label quality, initially for Fincrime teams, then for other squads in Servicing.
- Co-own stakeholder management, roadmap, delivery, and onboarding.
- Conduct presentations, demos, and workshops, maintain good documentation and progress updates.
- Drive impactful proof-of-concepts of new methodologies and tooling that bridge a gap for two or more teams in the Servicing tribe.
- Develop and maintain software engineering practices: testing + CI/CD, monitoring/alerting + disaster recovery.
- Implement MLOps practices including Terraform and AWS infrastructure, and ML governance for hundreds of models.
- Handle data engineering tasks involving distributed processing at terabyte scale.
- Prove the value of new methodologies/algorithms applied to cross-team domains, estimate and measure impact, and mentor junior members in experiment design.
What We're Looking For
- Extensive experience with end-to-end distributed data systems, especially ML-centric ones.
- 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 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.
Nice to Have
- Experience with Apache Spark, Airflow, Iceberg, Kafka, dbt.
- Familiarity with Scikit-Learn, XGBoost, MLFlow, Ray, PyTorch, Graph-tool (or similar).
- Experience with AWS (S3, EMR, SageMaker, Lakeformation), Terraform, Docker, GitHub CI/CD.
- Knowledge of Knowledge Graphs (+ RAG), graph ML, probabilistic programming, A/B testing.
Technical Stack
- Languages: Python, Java
- Data & Orchestration: Apache Spark, Airflow, Iceberg, Kafka, dbt
- ML & Science: Scikit-Learn, XGBoost, MLFlow, Ray, PyTorch, Graph-tool
- Infrastructure & Ops: AWS (S3, EMR, SageMaker, Lakeformation), Terraform, Docker, GitHub CI/CD
Team & Environment
You will join the Servicing Machine Learning and Data Engineering Team, part of the Servicing tribe.
Work Mode
This role is based locally in Tallinn or Budapest.
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