Freelance Data Science Engineer (Python & SQL)

Descrição da Vaga

Requisitos

• You hold a Master’s or PhD Degree in Data Science, Statistics, Mathematics, Computer Science, or related quantitative field
• You have at least 5 years of hands-on data science experience with proven business impact
• You have portfolio of completed projects and publications showcasing real-world problem-solving
• You are proficient in python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
• You are an expert in statistical analysis and machine learning with deep understanding of algorithms, methods, and their practical applications
• You are proficient in SQL and database operations for data manipulation and analysis
• You have experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
• You have good understanding of MLOps practices and model deployment workflows
• You possess knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
• Your level of English is advanced (C1) or above
• You are ready to learn new methods, able to switch between tasks and topics quickly and sometimes work with challenging, complex guidelines
• Our freelance role is fully remote so, you just need a laptop, internet connection, time available and enthusiasm to take on a challenge

Responsabilidades

• Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare)
• 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 (days/weeks)
• 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: avoid stochastic elements or require fixed random seeds for exact reproducibility
• 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 (data ingestion → cleaning → EDA → modeling → validation → deployment considerations)
• 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
• Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare)
• 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 (days/weeks)
• 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: avoid stochastic elements or require fixed random seeds for exact reproducibility
• 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 (data ingestion → cleaning → EDA → modeling → validation → deployment considerations)
• 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

Benefícios