Data Scientist (Python & SQL) - Freelance AI Trainer
Texas, United States. Michigan, United States. Minnesota, United States. Missouri, United States. South Carolina, United States. Wisconsin, United StatesContractMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningNumpyPyTorchPandasTensorflowscikit-learnPrompt EngineeringMLOpsGenerative AILangChain
Requirements
- 5+ years of hands-on data science experience with proven business impact
- Portfolio of completed projects and publications showcasing real-world problem-solving
- Expert Python programming for data science
- Experience with Pandas
- Experience with Numpy
- Experience with Scipy
- Experience with Scikit-learn
- Experience with Statsmodels
- Expert statistical analysis and machine learning
- Expert with SQL and database operations for data manipulation and analysis
- Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
- Understanding of MLOps practices and model deployment workflows
- Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
- Strong written English (C1+)
Responsibilities
- Design original computational data science problems that simulate real-world analytical workflows across industries
- Create problems requiring Python programming to solve (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn)
- Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes
- Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction
- Create deterministic problems with reproducible answers
- Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency
- Design end-to-end problems spanning the complete data science pipeline
- Incorporate big data processing scenarios requiring scalable computational approaches
- Verify solutions using Python with standard data science libraries and statistical methods
- Document problem statements clearly with realistic business contexts and provide verified correct answers
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