Data Science Manager
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Python Proficiency: You write clean, scalable, and modular code, understanding software engineering principles beyond one-off Jupyter Notebooks.
- LLM Experience: You have hands-on experience working with APIs from leading LLM providers.
- 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 mo
- Cloud & MLOps Familiarity: Knowledge of cloud platforms (AWS, Azure, GCP) and MLOps practices (CI/CD pipelines, containerization with Docker, monitoring tools) for deploying and productionizing AI app
- 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 approa
- 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 the
- 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 have
- 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.
Full description
Data Scientist (all genders)Location: Remote (Time zones: UTC / UTC +1 / UTC -1 / UTC +2 / UTC -2 / UTC +3 / UTC +4 / UTC +5)
Employment Type: Full-time
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 AI and knowledge structuring skills. Your mission is to automate the creation of structured medical education knowledge, leveraging your background in data science and computational methods. You will use Large Language Models (LLMs) to extract structured educational data from vast amounts of unstructured content, build the Knowledge Graph that powers adaptive learning pathways for medical students and lead our efforts to evaluate our AI-powered data products.
Key Responsibilities
- Context / Prompt Engineering: Design and iterate on complex LLM prompts (e.g., Chain-of-Thought, few-shot learning) to reliably extract defined entities (e.g., Concepts, Learning Objectives, Categories) and learning relationships from unstructured medical textbooks and transcripts.
- Graph Construction & Schema Definition: You will define the semantic schema and populate the chosen Graph Database (e.g., Neo4j/Cypher) to connect extracted medical facts directly to learning objectives (e.g., USMLE Step 1 goals).
- Validation Pipelines: Design and implement automated "judge" loops where an LLM or other automated checks evaluate the accuracy and logical consistency of its own extracted graph against gold-standard medical guidelines and our internal knowledge base.
- Evaluating AI features: Designing evaluation frameworks to assess an agent's performance, safety, and efficiency. This can involve measuring task completion success rates, the quality of the agent's reasoning steps, and its ability to recover from errors.
- 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
- Python Proficiency: You write clean, scalable, and modular code, understanding software engineering principles beyond one-off Jupyter Notebooks.