Senior Data Scientist

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

Descripción de la vacante

Clera is seeking a senior data scientist to join a fintech data products team in a hybrid data science and ML infrastructure role. You will build production models and establish evaluation, deployment, and monitoring foundations that keep ML systems reliable as they scale.

You will define evaluation frameworks, design experiments, and collaborate with data engineering on registries, feature stores, and drift monitoring. Strong Python/SQL skills and cloud tooling are required.

Formación

• 6+ years of data science or applied ML experience with production models.
• Strong knowledge of ML infrastructure, deployment, monitoring and retraining.
• Proficiency in Python and SQL, statistical modeling, and experiments.
• Experience establishing ML practices and standards on a team.
• Familiarity with Snowflake, dbt, PostgreSQL, Kubernetes, MLflow, Terraform, Prometheus and GCP.

Responsabilidades

• Define and evolve practices for evaluating, deploying, monitoring, versioning, and retraining models.
• Design evaluation frameworks and test harnesses to assess model quality pre- and post-launch.
• Build and own models for transaction coding, document understanding, and retrieval-augmented systems.
• Collaborate with data engineering on registries, feature stores, serving patterns, and drift monitoring.
• Set technical standards and improve ML tools and processes as the practice grows.
• Translate business needs into modeling questions and design experiments including A/B tests.

Conocimientos

Data science

Applied ML

Python

SQL

Experiment design

ML infrastructure

Model evaluation

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.