Principal Data Scientist – Machine Learning, AI

accelerant; Deutschland; Remote; EUR 85.000 - 140.000; Vollzeit

Zusammenfassung

Jobtailor is seeking an experienced data science professional to develop ML and AI systems that drive pricing, underwriting, portfolio management, operations, and claims. You will work across structured data, text, documents, and external sources, applying statistical modeling, modern ML and agentic workflows to solve real-world problems.

The role requires deep quantitative foundations, strong programming skills, and the ability to translate complex modeling concepts for both technical and

Qualifikationen

• Strong quantitative foundations in statistics, probability, or applied mathematics.
• Experience with real-world modelling and a track record of robust results, not just validation metrics.
• Proficiency in programming and software engineering.
• Comfort using LLMs and agentic AI as everyday tools.
• Ability to explain complex modelling concepts to both technical and non-technical audiences.
• Experience with advanced statistical methods (causal inference, experimental design, survival analysis, etc.).
• Insurance domain knowledge in pricing, underwriting, claims, or distribution is highly valued.
• Actuarial background or qualifications are a plus.
• Experience with model governance, explainability, and regulated environments.
• ML engineering experience from research to production and tooling for deployment.

Aufgaben

• Develop ML and AI systems to improve pricing, underwriting, portfolio management, operations and claims.
• Work with structured data, text, documents and external data sources to build scalable models.
• Identify approaches, build production-ready solutions and measure business impact.
• Tackle a broad range of ML/AI problems including predictive modelling, ranking and anomaly detection.

Kenntnisse

Statistics

Probability

Optimization

Applied Mathematics

Predictive Modeling

Classification

Anomaly Detection

Causal Inference

Survival Analysis

Hierarchical Models

Ausbildung

Actuarial Qualifications

Tools

AWS

Azure

GCP

Containers

Orchestration

APIs

Data Pipelines

Jobbeschreibung

• Develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations, and claims.
• Work across structured data, text, documents, and external data sources, applying statistical modeling, modern machine learning, AI and agentic workflows to solve challenging real-world problems.
• Identify the right approach, build production-ready solutions, and measure the business impact of your work.
• Tackle a broad range of machine learning and AI problems such as predictive modeling, classification, ranking, matching, recommendation, anomaly detection, information extraction, entity resolution, building high-quality datasets, and automating analytical and decision-making workflows.

Requirements

• A strong quantitative foundation: statistics, probability, optimisation, or applied mathematics
• Sound modelling judgement - you know what it takes for a model to hold up in the real world, not just on a validation set
• Strong programming skills
• Real willingness to work with LLMs and agentic AI as everyday tools, wherever your background sits today
• Clear communication with both technical and non-technical audiences - you can explain a lift curve to an underwriter and a shrinkage prior to a statistician
• Experience in one or more of the following is especially valuable: Track record with LLM-powered applications or AI agents, especially if you've done the unglamorous work of proving they perform Depth in the statistical toolkit beyond supervised prediction: hierarchical models and shrinkage estimation, causal inference and experimentation, survival analysis, extreme value theory, or demand and elasticity modelling
• Insurance domain knowledge: pricing, reserving, claims, underwriting, or distribution
• Actuarial background or qualifications (partially or fully qualified)
• Experience in regulated industries where model governance and explainability matter
• ML engineering experience: taking models from research code to production services, or building the tooling and frameworks that help others deploy
• Cloud and infrastructure skills: AWS, Azure, or GCP; containers and orchestration; APIs and data pipelines built with cost, latency, and reliability in mind
• MLOps in practice: experiment tracking, model monitoring, automated retraining, and CI/CD for models and agent

Core Competencies

Demonstrates expertise in developing machine learning and AI systems, with a strong quantitative foundation in statistics and applied mathematics. Proficient in ML engineering, model governance, and cloud technologies, particularly in the insurance domain.

Highest-signal resume keywords

• Machine Learning Development
• Statistical Modeling
• LLM-Powered Applications
• Cloud Technologies (AWS, Azure, GCP)
• MLOps Practices

ATS Optimization Keywords

Hard Skills

• Statistics
• Probability
• Optimization
• Applied Mathematics
• Predictive Modeling
• Classification
• Anomaly Detection
• Causal Inference
• Survival Analysis
• Hierarchical Models

Soft Skills

• Clear Communication
• Sound Modelling Judgement

Certifications & Qualifications

• Actuarial Qualifications

Industry Keywords

• Insurance Domain Knowledge
• Pricing
• Underwriting
• Claims
• Model Governance

Tools & Technologies

• AWS
• Azure
• GCP
• Containers
• Orchestration
• APIs
• Data Pipelines