Data Scientist Lead

Information about the project:

• Industry: pharma

• Location: fully remote

• Rate: approx. 230-280 netto + VAT (B2B)

• Long-term cooperation

What You’ll Do:

• Build and evaluate generative AI, machine learning, and NLP models that deliver real business value

• Maintain and optimize analytics solutions for high performance and reliability

• Extract insights from structured and unstructured data to support Product Supply decisions

• Develop prototypes, then scale them into industrialized IT products

• Create user-friendly data visualizations, dashboards, and scenario-planning tools

• Develop reusable frameworks and libraries that streamline data science workflows

• Explore new data sources, tools, and techniques through innovation projects

• Build data pipelines and support data preparation for modeling

• Collaborate through GitHub, including code reviews and shared development

• Contribute to continuous learning through workshops and knowledge-sharing

• Present data-driven insights to stakeholders across all levels

• Act as a subject matter expert in AI and data science best practices

What You Bring

• Master’s or PhD degree and 4+ years of experience in Data Science, AI Engineering, or similar fields

• Strong educational or practical background in ML, statistics, mathematics, computer science, bioinformatics, or related quantitative disciplines

• Solid experience with generative AI and machine learning

• Advanced Python skills with experience in web application development

• Understanding of LLMs, Retrieval-Augmented Generation (RAG), and AI agent architectures

• Experience designing and implementing AI agent systems

• Working knowledge of CI/CD, Azure, Docker, FastAPI/Flask, and Databricks

• (Nice to have) Experience with LangChain, LangGraph, LlamaIndex

• Ability to write production-grade OOP code

• Hands-on experience developing advanced analytics in cloud environments

• Strong problem-solving, communication, and presentation skills

• Ability to create thorough documentation