Data Scientist - AI & Experimentation (m/f/d)

At Pflegia we are building and operating an innovative job-matching platform, which intelligently brings together caregivers and healthcare employers. Our vision is to become Europe's leading job platform for nursing professions and to fight the nursing crisis! Become part of the team and shape the nursing job market together with us!

About the Role

We're looking for a Data Scientist who treats AI as a working tool, not a buzzword. You'll sit at the intersection of statistics, machine learning, and product: building predictive models, improving our LLM- and RAG-based systems, and running experiments that directly shape how our platform matches supply and demand. Your work won't end at a slide deck. You'll define the metrics, ship the analysis, and follow through until the impact shows up in the numbers.

Tasks

• Build, validate, and ship statistical and predictive models that directly inform pricing, matching, and growth decisions

• Develop and improve LLM-powered features, from retrieval-augmented generation (RAG) pipelines to applications of new AI technologies that open up product innovation

• Own the reliability of our AI features: design prompt and evaluation workflows, measure output quality, and catch regressions before users do

• Turn open questions into testable hypotheses and design experiments (e.g., A/B tests) that give clear, decision-ready answers

• Dig into funnels and user journeys to find drop-offs and friction points, and quantify where supply and demand can be better matched

• Team up with performance marketing to sharpen targeting, attribution, and campaign efficiency with data

• Define the KPIs that matter, build the dashboards and monitoring behind them (AWS QuickSight), and make business impact visible and measurable

• Keep your work transparent and traceable: document, prioritize, and communicate progress in Jira across product, engineering, and marketing

• Present findings to stakeholders as concrete recommendations, then stay involved until they're implemented

Requirements

• You love to work with data: explore it, model it, improve its quality.

• Deep grounding in statistics: you know which method fits which problem and can defend your assumptions, not just run the library defaults

• Fluent in Python (pandas, scikit-learn, NumPy) and SQL, with a track record of applying them to real business problems rather than toy datasets

• Hands-on experience taking ML and modern AI techniques from idea to a working solution that someone actually uses

• Practical experience with LLMs and RAG systems in production or near-production settings, including prompting, retrieval quality, and output evaluation