Senior ML Engineer, FinTech (Remote EU)

Clera; United States; Remote; USD 84,000 - 112,000; Full time

Job summary

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.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Data science

Applied ML

Python

SQL

Experiment design

ML infrastructure

Model evaluation

Deployment monitoring

Tools

Snowflake

dbt

PostgreSQL

Kubernetes

MLflow

Terraform

Prometheus

Google Cloud

Job description

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.