Data Scientist

About Us

Lecturio is a leading international medical and nursing e-learning platform, serving students as well as educational institutions. Lecturio’s mission is to address the severe shortage of healthcare providers around the world through increasing access to quality medical education. Using state-of-the-art technology and learning science, Lecturio has grown from its headquarters in Leipzig, Germany, where it was founded in 2008, to become a global leading digital teaching engine for health professions education.

Data Scientist (all genders)

We are looking for a Data Scientist with strong analytical, research, and scientific reasoning skills. Your mission is to establish rigorous methodologies for structuring medical education knowledge, analyzing complex datasets, and testing educational hypotheses. Leveraging your background in data science, statistical analysis, and applied research, you will formulate hypotheses, design validation frameworks, and evaluate AI-driven educational products with scientific rigor.

Key Responsibilities

• Hypothesis Testing & Research Methodology: Formulate testable hypotheses regarding educational structures, conceptual relationships, and learning outcomes, designing structured experiments to evaluate them rigorously.
• Data Modeling & Semantic Mapping: Analyze domain data to determine optimal data structures, define semantic schemas, and map complex medical content into structured knowledge representations.
• Statistical Evaluation & Validation Frameworks: Design validation methodologies, metrics, and quality benchmarks to systematically measure the accuracy, logical consistency, and statistical validity of structured knowledge models against gold-standard medical guidelines.
• Performance Analysis & Controlling: Develop measurable indicators and control mechanisms to evaluate AI feature performance, reasoning quality, and failure modes across different environments.
• Collaboration: Work with medical experts to validate extraction accuracy and partner with the platform engineering team to integrate the resulting Knowledge Graph API into our core product features.

Your profile

• Analytical & Statistical Rigor: Strong background in statistical analysis, experimental design, and quantitative evaluation techniques.
• Scientific & Exploratory Mindset: Proven ability to analyze unstructured problems, perform systematic literature or data research, and derive actionable data-driven insights.
• Structured Data Mindset: You possess a strong understanding of why data structures are critical for machine consumption, as opposed to relying on unstructured text output.
• Knowledge of or Willingness to Learn Graph DBs: You may not know Neo4j or Cypher yet, but you demonstrate exceptional enthusiasm and aptitude to master graph database technologies within your first months.
• Data Prototyping & Scripting: Proficient in Python for data analysis, statistical computing, and rapid proof-of-concept validation.
• Communication Skills: You have the ability to communicate clearly and effectively in English, both verbally and in writing, to understand requirements, and articulate technical concepts.
• Collaborative Spirit: You are a strong team player who actively shares ideas and embraces diverse perspectives. While expertise is highly valued and decisions will be respected, a collaborative approach is essential.
• Accountability: Proactive foresight in defining expected outcomes, timelines, and effort. You will be expected to communicate any changes to estimates promptly and take full ownership of achieving their objectives.
• A Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics or a similar program.
• Demonstrated impact and scope in your previous roles (e.g., creating scalable solutions, influencing product direction, etc.).
• Ownership: You own a problem and go beyond your scope to find a solution.

Nice-to-Haves:

• Familiarity with medical terminologies and taxonomies (MeSH, SNOMED, ICD-10).
• A proven interest in Pedagogy, Learning Science, or Adaptive Learning Systems.
• Previous experience defining or modeling relational data schemas.

You should be able to:

• Make an Observation: Notice and record a specific phenomenon or pattern in the data and understand patterns.
• Ask a Question: Define the specific problem or inquire about the cause of the observed behavior.
• Form a Hypothesis: