Long-Term Python Predictive Analyst- Data analystic for computer research writing
Budget: ₹12,500 – ₹37,500 INR
I am conducting an ongoing series of studies that investigate new machine-learning algorithms for computer-science and electronics applications. To keep the research engine running, I need a Python-savvy data analyst who can turn structured datasets into solid predictive models and then translate the results into clear, publication-ready writing.
Your typical workflow will involve cleaning and feature-engineering data in pandas, building and evaluating models with scikit-learn (and, when helpful, TensorFlow or PyTorch), and producing insightful visualisations. Once the analytics are sound, I’ll count on you to craft well-structured manuscripts—complete with methodology, metrics, figures, and proper citations—that can be submitted to journals or conferences in the IEEE ecosystem.
Expect to work in a reproducible environment (Git, virtual envs, Jupyter notebooks) and to perform literature reviews so each experiment builds logically on the last. If you can justify model choices with solid statistics, propose improvements, and explain them in crisp prose, we should be a perfect match.
Deliverables for each study:
• Clean, commented Python code or notebooks
• A brief methodology section detailing data prep, model selection, and evaluation metrics
• A 3–5 page research draft summarising findings, figures, and references
• Publication-ready charts or tables
Clear communication, reliable turnaround, and meticulous documentation are essential. If you’re ready to start with the first dataset and grow this collaboration over the long term, I’d love to hear from you.
Your typical workflow will involve cleaning and feature-engineering data in pandas, building and evaluating models with scikit-learn (and, when helpful, TensorFlow or PyTorch), and producing insightful visualisations. Once the analytics are sound, I’ll count on you to craft well-structured manuscripts—complete with methodology, metrics, figures, and proper citations—that can be submitted to journals or conferences in the IEEE ecosystem.
Expect to work in a reproducible environment (Git, virtual envs, Jupyter notebooks) and to perform literature reviews so each experiment builds logically on the last. If you can justify model choices with solid statistics, propose improvements, and explain them in crisp prose, we should be a perfect match.
Deliverables for each study:
• Clean, commented Python code or notebooks
• A brief methodology section detailing data prep, model selection, and evaluation metrics
• A 3–5 page research draft summarising findings, figures, and references
• Publication-ready charts or tables
Clear communication, reliable turnaround, and meticulous documentation are essential. If you’re ready to start with the first dataset and grow this collaboration over the long term, I’d love to hear from you.