Senior Data Scientist

Clera; United States; A distancia; USD 84.000 - 112.000; Jornada completa

Descripción de la vacante

The role emphasizes designing evaluation frameworks, A/B and causal analyses, and shaping standards as the ML practice grows across cloud platforms and Kubernetes-based environments.

Formación

• 6+ years of data science or applied ML experience.
• Experience shipping models to production and maintaining ML systems.

Responsabilidades

• 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.

Conocimientos

Python

SQL

Statistical modeling

Experiment design

Applied ML in prod

ML deployment & monitoring

Herramientas

Snowflake

dbt

PostgreSQL

Kubernetes

MLflow

Terraform

Prometheus

Google Cloud

Descripción del empleo

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.