数据科学家 - 人工智能与实验(男/女/其他)
Data Scientist - AI & Experimentation (m/f/d)
在Pflegia,我们正在打造并运营一个创新的职位匹配平台,智能地将护理人员和医疗雇主联系在一起。我们的愿景是成为欧洲领先的护理职业招聘平台,解决护理人员短缺问题!加入我们的团队,与我们一起塑造护理岗位市场!
职位简介
我们正在寻找一位数据科学家,他将AI视为一种实用工具,而非流行术语。你将处于统计学、机器学习和产品开发的交汇点:构建预测模型,改进基于大语言模型(LLM)和检索增强生成(RAG)的系统,并开展直接影响我们平台供需匹配的实验。你的工作不会止于幻灯片。你将定义指标、发布分析,并持续跟进直到效果体现在数据中。
职责
- 构建、验证并部署直接支持定价、匹配和增长决策的统计和预测模型
- 开发并优化基于大语言模型的功能,从检索增强生成(RAG)流程到应用新的AI技术以推动产品创新
- 负责我们AI功能的可靠性:设计提示和评估流程,衡量输出质量,并在用户发现之前捕捉回归问题
- 将开放性问题转化为可测试的假设,并设计实验(如A/B测试),提供明确且可决策的答案
- 分析漏斗和用户旅程,找出流失点和摩擦点,并量化供需如何更好地匹配
- 与绩效营销团队合作,通过数据提升定位、归因和活动效率
- 定义关键绩效指标(KPI),构建其背后的仪表盘和监控(AWS QuickSight),使业务影响可见且可衡量
- 保持工作的透明和可追溯:在Jira中记录、优先处理并跨产品、工程和营销团队沟通进展
- 向利益相关者呈现具体建议,然后持续跟进直至实施
要求
- 你热爱与数据打交道:探索它、建模它、提升它的质量
- 深厚的统计学基础:你知道哪种方法适合哪种问题,并能捍卫自己的假设,而不仅仅是运行库的默认值
- 精通Python(pandas、scikit-learn、NumPy)和SQL,有将它们应用于真实商业问题而非玩具数据集的经验
- 有从想法到实际解决方案的ML和现代AI技术实战经验
查看英文原文
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
- Solid command of A/B testing: sample sizing, significance, common pitfalls, and knowing when an experiment is the wrong tool
- Working knowledge of performance marketing concepts such as CAC, ROAS, and attribution logic
- Project experience in at least one of: anomaly detection, trend analysis, marketing mix modeling, or multi-touch attribution
- Background in e-commerce, marketplaces, or other platform-based businesses, ideally with exposure to supply and demand dynamics
- Bonus: degree in mathematics, statistics, physics, computer science, or a related quantitative field
Benefits
- Flat hierarchies with short decision-making paths (start-up mentality) and an open corporate culture with helpful & communicative colleagues
- Regular feedback conversations
- A pleasant workplace (open-plan office centrally located in Berlin-Mitte) with home office option
- A very nice and cooperative working atmosphere
- Free drinks
Sound like you? Send us your CV and a short note on the most interesting model or experiment you've shipped recently.
Originally posted on Himalayas