About the role
About the Role
Join a fintech data products team in a hybrid data science and ML infrastructure role. You will build production models and establish the evaluation, deployment, and monitoring foundations that help ML systems remain reliable as they scale.
What You'll Do
Define and evolve practices for evaluating, deploying, monitoring, versioning, and retraining models.
Design evaluation frameworks and test harnesses to assess model quality before and after launch.
Build and own models and analyses for transaction coding suggestions, document understanding, and retrieval-augmented or agentic systems.
Partner with data engineering on infrastructure decisions, including model registries, feature stores, serving patterns, and monitoring for drift and degradation.
Set technical standards and improve tools and processes as the ML practice grows.
Translate ambiguous business needs into modeling questions and design experiments, including A/B tests and causal analyses, to measure impact.
What We're Looking For
At least 6 years of data science or applied ML experience, including models shipped to production.
Strong knowledge of ML infrastructure, including deployment, monitoring, retraining, model registries, and feature stores, plus experience designing evaluation frameworks.
Proficiency in Python and SQL, statistical modeling, and experiment design.
Experience establishing ML or data science practices and standards on a team.
Relevant technologies include Snowflake, dbt, PostgreSQL, Kubernetes, MLflow, Terraform, Prometheus, and Google Cloud services; experience with cloud ML platforms and workflow orchestrators is useful.
Experience with RAG, agents, OCR, or document understanding is useful. Fintech, banking, or financial data experience is also valuable.
Compensation & Benefits
Salary range: €75,000 to €100,000 annually.
Location
Fully remote within the EU.
Originally posted on Himalayas