Public summary
Seeking experienced freelance Data Science Engineer for project-based AI tasks with leading tech companies in Germany. Role involves designing complex data science problems involving Python programming and SQL, focusing on real-world business challenges across various industries such as telecom, finance, and healthcare. Moderate part-time workload (10-20 hours/week) during active project phases with flexible engagement and competitive pay up to $58/hour. Expertise in statistical analysis, machine learning, and GenAI technologies required. English proficiency (C1+) mandatory; no German language requirement specified.
Salary
USD 58.00 - 58.00 hour
Responsibilities
Design computational data science problems simulating real-world scenarios across industries; create Python-based problems involving extensive data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction; ensure problems are computationally intensive and reproducible; base problem cases on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, and operational efficiency; cover end-to-end data science pipeline stages including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations; incorporate big data processing requiring scalable approaches; verify solutions using Python libraries and statistical methods; document clear problem statements with realistic business contexts and verified solutions.
Qualifications
Minimum 5 years hands-on data science experience with proven business impact; portfolio demonstrating real-world problem-solving projects and publications; expert proficiency in Python for data science (pandas, numpy, scipy, scikit-learn, statsmodels); strong statistical analysis and machine learning knowledge including algorithms and practical applications; expert skills in SQL and database operations for data manipulation and analysis; experience with GenAI technologies including large language models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases; understanding of MLOps practices and model deployment workflows; familiarity with modern frameworks such as TensorFlow, PyTorch, and LangChain; strong written English skills at C1 level or higher.