Data Scientist

omni:us

. Design and build LLM-based agents and assistants for insurance workflows, including claims intake and triage, coverage checks, document and correspondence processing, fraud indicators and case summarisation .

Posted 11/12/2026full-timeRemote • GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and building LLM-based agents for insurance workflows, with strong capabilities in prompt engineering, evaluation frameworks, and compliance with GDPR and the EU AI Act. Proficient in Python and ML libraries, with a solid understanding of data engineering and the ability to analyze multilingual data effectively.

Highest-signal resume keywords

LLM Application DevelopmentPython ProgrammingGenerative AI EvaluationData Engineering ConceptsNLP and Document Understanding

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ScienceMachine LearningArtificial IntelligenceLLM APIsMultimodal ModelsETLDatabasesVector StoresPrompt EngineeringEvaluation Frameworks

Soft Skills

Analytical AbilitiesProblem-SolvingCommunication SkillsDetail-OrientedSelf-Driven

Tools & Technologies

PyTorchHugging FaceScikit-learnDockerAWSAzureLinuxBashLangGraphLlamaIndex

Industry Keywords

Insurance WorkflowsClaims IntakeFraud IndicatorsGDPR ComplianceEU AI ActBusiness Impact MeasurementAgent FrameworksMultilingual Data AnalysisCost and Latency TrackingHuman-in-the-Loop

Tech Stack

Tools & technologies

AWSAzureCloudDockerETLLinuxPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Design and build LLM-based agents and assistants for insurance workflows, including claims intake and triage, coverage checks, document and correspondence processing, fraud indicators and case summarisation
Develop agentic workflows combining LLMs with tools, APIs, business rules and retrieval over policies, claim files and knowledge bases using RAG
Implement multi-step reasoning and human-in-the-loop handover
Apply and adapt multimodal models to extract information from complex insurance documents, including forms, invoices, reports, emails and images
Build evaluation frameworks for generative and agentic systems, including test sets, automated and LLM-as-a-judge evaluation, accuracy and hallucination metrics, cost and latency tracking, and production monitoring
Work on prompt and context engineering, and fine-tune or distill models for specific insurance tasks where beneficial
Design guardrails for safe, explainable and auditable AI decisions aligned with GDPR and the EU AI Act
Collaborate with product managers, engineers and insurance domain experts to bring solutions into production and measure business impact
Assess developments in generative and agentic AI, new models and frameworks, and integrate useful technologies into products
Document approaches, experiments and results for internal use and client communication

Requirements

What you’ll need

Bachelor or Master degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, or a related field
3+ years of experience in data science or machine learning, with a proven track record of delivering AI solutions to production
Hands-on experience building applications with LLMs, including RAG, tool use/function calling, structured outputs, and agents
Experience with the insurance or financial industry is a plus but not required
Strong Python skills and experience with ML/DL libraries such as PyTorch, Hugging Face or scikit-learn
Experience with LLM APIs and agent frameworks or protocols such as LangGraph, LlamaIndex, Model Context Protocol, or similar
Good understanding of evaluating and monitoring generative AI systems, including hallucinations, non-determinism and cost
Familiarity with NLP, document understanding and/or multimodal models
Solid understanding of data engineering concepts, including ETL, databases and vector stores
Experience with Linux, bash, Docker and cloud platforms such as AWS and Azure is beneficial
Proficiency in German and English, written and spoken
Ability to work with and analyze multilingual data effectively
Strong analytical and problem-solving abilities
Excellent communication skills for working with technical teams, domain experts and clients
Self-driven and detail-oriented, with a pragmatic, product-oriented mindset and skepticism toward hype

Benefits

Comp & perks

Competitive salary
Comprehensive benefits
Access to state-of-the-art models, tools and infrastructure
Flexible work arrangements, including remote and hybrid options
Impactful projects that go into production with real clients and real data
Inclusive and diverse workplace