Freelance Data Science Engineer (Python & SQL)

About the Role

The role requires strong technical skills, including proficiency in Python and SQL, as well as experience in machine learning and statistical analysis. You will be working on complex data science problems, designing end-to-end problems that span the complete data science pipeline, from data ingestion to deployment.

What You Will Do

• Design original computational data science problems that simulate real-world analytical workflows across industries
• Create problems requiring Python programming to solve, using libraries such as Pandas, Numpy, and Scikit-learn
• Develop problems that require non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction
• Create deterministic problems with reproducible answers, avoiding stochastic elements or requiring fixed random seeds for exact reproducibility
• Base problems on real business challenges, such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency
• Design end-to-end problems that span the complete data science pipeline, from data ingestion to deployment
• Incorporate big data processing scenarios that require scalable computational approaches
• Verify solutions using Python with standard data science libraries and statistical methods
• Document problem statements clearly, with realistic business contexts and verified correct answers

What We Are Looking For

• 5+ years of hands-on data science experience, with proven business impact
• Portfolio of completed projects and publications, showcasing real-world problem-solving skills
• Expert Python programming skills, with proficiency in libraries such as Pandas, Numpy, and Scikit-learn
• Expert statistical analysis and machine learning skills, with experience in algorithms, methods, and practical applications
• Strong SQL skills, with experience in database operations for data manipulation and analysis
• Experience with GenAI technologies, such as LLMs, RAG, prompt engineering, and vector databases
• Understanding of MLOps practices and model deployment workflows
• Knowledge of modern frameworks, such as TensorFlow, PyTorch, and LangChain
• Strong written English skills, with a C1+ level of proficiency

Nice to Have

• Experience working with big data processing scenarios and scalable computational approaches
• Knowledge of cloud-based data platforms, such as AWS or Google Cloud
• Experience with containerization, using tools such as Docker
• Familiarity with agile development methodologies and version control systems, such as Git

Benefits and Perks

• Competitive hourly rate, with the potential to earn up to $58 per hour equivalent
• Flexible, project-based work arrangement, with the ability to work remotely
• Opportunity to work on a variety of projects, with leading tech companies
• Professional development opportunities, with access to training and resources
• Potential for long-term collaboration, with the opportunity to work on multiple projects
• Access to a community of skilled data science professionals, with opportunities for networking and collaboration

• Make sure to highlight your Python and SQL skills, as well as your experience in machine learning and statistical analysis.
• Showcase your portfolio of completed projects and publications, to demonstrate your real-world problem-solving skills.
• Be prepared to discuss your experience working with big data processing scenarios and scalable computational approaches.
• Emphasize your understanding of MLOps practices and model deployment workflows, as well as your knowledge of modern frameworks.
• Don't be afraid to negotiate your hourly rate, based on your level of experience and expertise.
• Research the company and the role, to understand the types of projects you will be working on and the skills required.
• Practice your communication skills, to effectively discuss complex data science problems and solutions.