Data Scientist - AI & Experimentation.

Accountabilities:

You will lead data science initiatives from problem definition and modeling through experimentation, deployment, and performance monitoring. Your responsibilities will include:

• Develop, validate, and deploy statistical and predictive models that inform pricing, job matching, and growth decisions.

• Build and improve LLM-powered product features, including Retrieval-Augmented Generation (RAG) pipelines and applications of emerging AI technologies.

• Design prompt engineering and evaluation workflows to assess AI output quality, monitor reliability, and identify performance regressions before they affect users.

• Translate business questions into testable hypotheses and design experiments, including A/B tests, that support clear, evidence-based decisions.

• Analyze conversion funnels and user journeys to identify drop-off points, uncover friction, and improve matching between healthcare professionals and employers.

• Partner with performance marketing teams to optimize audience targeting, attribution, and campaign efficiency.

• Define meaningful key performance indicators (KPIs) and build dashboards and monitoring systems using tools such as AWS QuickSight.

• Track model performance, experiment outcomes, and business impact to ensure data initiatives deliver measurable results.

• Document projects, prioritize tasks, and communicate progress transparently through Jira and other collaborative workflows.

• Present findings and actionable recommendations to stakeholders across product, engineering, and marketing.

• Follow through on recommendations to support implementation and measure their impact after deployment.

Requirements:

The ideal candidate combines strong statistical foundations with practical machine learning and modern AI experience. You should be comfortable working independently with data, selecting appropriate analytical methods, and translating technical results into business decisions. The key qualifications include:

• Strong knowledge of statistics, including the ability to select appropriate methods, justify assumptions, and interpret results critically.

• Advanced proficiency in Python, particularly pandas, scikit-learn, and NumPy, as well as strong SQL skills.

• Demonstrated experience applying statistical analysis and data science techniques to real-world business problems.

• Hands-on experience taking machine learning models and modern AI solutions from initial concept through implementation and practical use.

• Practical experience with LLMs and RAG systems in production or near-production environments, including prompt engineering, retrieval quality, and output evaluation.

• Solid understanding of A/B testing, including sample size estimation, statistical significance, common experimental pitfalls, and situations where experimentation may not be appropriate.

• Working knowledge of performance marketing metrics and concepts, including Customer Acquisition Cost (CAC), Return on Ad Spend (ROAS), and marketing attribution.

• Project experience in at least one of the following areas: anomaly detection, trend analysis, marketing mix modeling, or multi-touch attribution.

• Experience in e-commerce, online marketplaces, or other platform-based businesses, ideally involving supply-and-demand dynamics.

• Strong analytical thinking, problem-solving abilities, and attention to data quality.

• Ability to communicate complex findings clearly, make practical recommendations, and collaborate effectively with cross-functional stakeholders.

• Experience documenting work, managing priorities, and tracking projects using Jira or similar tools.

• A degree in mathematics, statistics, physics, computer science, or another quantitative discipline is an advantage.

Benefits:

• Annual salary ranging from €42,000 to €75,000.

• Permanent employment opportunity.

• Remote work arrangement, with a home-office option.

• Flat organizational structure, short decision-making processes, and a start-up mindset.

• Open and collaborative company culture with supportive, communicative colleagues.

• Regular feedback conversations to encourage professional development.

• Access to a centrally located office in Berlin-Mitte, with an open-plan working environment.

• Complimentary drinks at the office.

• Opportunity to work on practical AI, machine learning, and experimentation projects with a direct impact on product and business performance.