Description review
Senior Data Scientist
Clera · remote · back to the listing
HR standards
75/100
solid
Title ↔ description
88/100
strong
Reads as
Unclear
no confident match
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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- Data Scientist, Analytics (Technical Leadership) Meta
Startups
- Amplify Renewables | Quantitative Researcher | SF / Seattle / US | Hybrid (Remote Possible) | Full-Time | $200k - $375k (all cash) Amplify Renewables
- Senior Data Scientist BoldVoice
- Senior Research Neuroscientist ANORIA
- Quantitative Associate Corgi Insurance
- Senior Data Scientist Coulomb AI
What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.