Comprehensive Data Science Help
Budget: ₹12,500 – ₹37,500 INR
I need an experienced data scientist to partner with me from the ground up. The project’s exact objective is still open—we might build a predictive model, create in-depth visual analytics, or even explore natural-language insights—so I’m looking for someone comfortable helping shape the scope before diving into the work.
Right now, the raw information lives in more than one place. Some of it is tucked away in a relational database, other parts may arrive through an external API, and I also have flat files (CSV/Excel) that will need cleaning. Whether the data turn out to be neatly structured, completely unstructured, or a blend of both, I’ll rely on your expertise to ingest, wrangle, and prepare it for analysis.
Typical tool-sets such as Python (pandas, scikit-learn, spaCy), R, SQL, Jupyter, and visualization libraries like Matplotlib, Seaborn, or Power BI are all welcome, so feel free to suggest the stack you’re most productive with.
Deliverables
• A brief strategy outline summarising the agreed objective and approach
• Reproducible, well-commented code or notebooks covering data cleaning, exploration, and modelling/visualisation
• A concise written report (or slide deck) presenting key findings, metrics, and actionable recommendations
• Final assets packaged so I can re-run everything locally without surprises
Acceptance criteria
• Data pipelines run end-to-end on my machine with clear instructions
• Insights or model performance meet the targets we agree on during kick-off
• All work follows good version-control practices and includes documentation
If you enjoy turning messy, multi-source data into solid, decision-ready insight, I’d love to hear how you would tackle this from day one.
Right now, the raw information lives in more than one place. Some of it is tucked away in a relational database, other parts may arrive through an external API, and I also have flat files (CSV/Excel) that will need cleaning. Whether the data turn out to be neatly structured, completely unstructured, or a blend of both, I’ll rely on your expertise to ingest, wrangle, and prepare it for analysis.
Typical tool-sets such as Python (pandas, scikit-learn, spaCy), R, SQL, Jupyter, and visualization libraries like Matplotlib, Seaborn, or Power BI are all welcome, so feel free to suggest the stack you’re most productive with.
Deliverables
• A brief strategy outline summarising the agreed objective and approach
• Reproducible, well-commented code or notebooks covering data cleaning, exploration, and modelling/visualisation
• A concise written report (or slide deck) presenting key findings, metrics, and actionable recommendations
• Final assets packaged so I can re-run everything locally without surprises
Acceptance criteria
• Data pipelines run end-to-end on my machine with clear instructions
• Insights or model performance meet the targets we agree on during kick-off
• All work follows good version-control practices and includes documentation
If you enjoy turning messy, multi-source data into solid, decision-ready insight, I’d love to hear how you would tackle this from day one.