{"id":3463834,"url":"https://alion.io/job/pflegia-data-scientist-ai-experimentation-mfd-2","title":"Data Scientist - AI & Experimentation (m/f/d)","company":{"id":173388,"name":"Pflegia","domain":"pflegia.de","url":"https://alion.io/company/pflegia","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Join","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Jira","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2025-08-05T19:27:44Z","employer_posted_date":"2025-08-05","last_verified_at":"2026-11-04T01:06:57Z","board_verified":true,"closed_at":null,"days_open":455,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":455},"description":"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!\nAbout the Role\nWe'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.\nTasks\nBuild, validate, and ship statistical and predictive models that directly inform pricing, matching, and growth decisions\nDevelop and improve LLM-powered features, from retrieval-augmented generation (RAG) pipelines to applications of new AI technologies that open up product innovation\nOwn the reliability of our AI features: design prompt and evaluation workflows, measure output quality, and catch regressions before users do\nTurn open questions into testable hypotheses and design experiments (e.g., A/B tests) that give clear, decision-ready answers\nDig into funnels and user journeys to find drop-offs and friction points, and quantify where supply and demand can be better matched\nTeam up with performance marketing to sharpen targeting, attribution, and campaign efficiency with data\nDefine the KPIs that matter, build the dashboards and monitoring behind them (AWS QuickSight), and make business impact visible and measurable\nKeep your work transparent and traceable: document, prioritize, and communicate progress in Jira across product, engineering, and marketing\nPresent findings to stakeholders as concrete recommendations, then stay involved until they're implemented\nRequirements\nYou love to work with data: explore it, model it, improve its quality.\nDeep grounding in statistics: you know which method fits which problem and can defend your assumptions, not just run the library defaults\nFluent in Python (pandas, scikit-learn, NumPy) and SQL, with a track record of applying them to real business problems rather than toy datasets\nHands-on experience taking ML and modern AI techniques from idea to a working solution that someone actually uses\nPractical experience with LLMs and RAG systems in production or near-production settings, including prompting, retrieval quality, and output evaluation\nSolid command of A/B testing: sample sizing, significance, common pitfalls, and knowing when an experiment is the wrong tool\nWorking knowledge of performance marketing concepts such as CAC, ROAS, and attribution logic\nProject experience in at least one of: anomaly detection, trend analysis, marketing mix modeling, or multi-touch attribution\nBackground in e-commerce, marketplaces, or other platform-based businesses, ideally with exposure to supply and demand dynamics\nBonus: degree in mathematics, statistics, physics, computer science, or a related quantitative field\nBenefits\nFlat hierarchies with short decision-making paths (start-up mentality) and an open corporate culture with helpful & communicative colleagues\nRegular feedback conversations\nA pleasant workplace (open-plan office centrally located in Berlin-Mitte) with home office option\nA very nice and cooperative working atmosphere\nFree drinks\nSound like you? 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