Public summary
Seeking a freelance Data Science Engineer skilled in Python and SQL for part-time AI projects with leading tech companies. The role involves designing and solving complex data science problems based on real-world business challenges across multiple industries, using advanced Python libraries and machine learning frameworks. Candidates should have 5+ years of hands-on data science experience, strong expertise in statistical analysis, machine learning, and SQL, plus familiarity with cutting-edge GenAI technologies and MLOps. Excellent written English (C1+) is required. This is a project-based, non-permanent opportunity offering pay up to $58 per hour equivalent depending on contribution.
Salary
USD 58.00 - 58.00 hour
Responsibilities
Design original computational data science problems simulating real-world analytical workflows across various industries; create Python programming problems using key data science libraries; ensure problems require intensive computation and advanced reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction; develop deterministic problems with reproducible answers; base problems on realistic business scenarios such as customer analytics, risk assessment, fraud detection, forecasting, and optimization; cover the complete data science pipeline including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations; incorporate scalable big data processing approaches; verify and document solutions with realistic context and correct answers.
Qualifications
Minimum 5 years of practical data science experience with demonstrated business impact; portfolio showcasing completed projects and publications; expert Python programming skills with libraries like pandas, numpy, scipy, scikit-learn, and statsmodels; deep understanding of statistical analysis and machine learning algorithms; expert-level SQL and database operations skills; experience with GenAI technologies including large language models, retrieval-augmented generation, prompt engineering, and vector databases; familiarity with MLOps practices and model deployment workflows; knowledge of frameworks such as TensorFlow, PyTorch, and LangChain; strong written English proficiency at C1 level or above.