Show models that made it past the notebook

What data scientists in India should know before going online

Data science hiring in India covers product companies, banks and NBFCs, e-commerce, analytics consultancies and global capability centres, and job titles overlap with analytics and ML engineering roles. Recruiters often filter by Python, SQL and machine learning keywords, then send a take-home assignment, so a site with real modelling work gives you a head start. Many candidates show the same public datasets, so a project on Indian data, such as regional language text, monsoon-linked sales or public transport records, stands out. Banks and insurers here handle sensitive customer data under privacy rules, so never publish employer data even if anonymised by you. Kaggle rankings and course certificates help freshers, but hiring managers give more weight to clear problem framing and honest evaluation.

Common website mistakes data scientists make

• Showing only accuracy numbers. Explain the baseline, the metric that matters for the business and where the model fails.
• Filling the site with course projects on famous public datasets. Keep one if you must, and lead with an original problem.
• Publishing notebooks that do not run. Pin package versions, include sample data or a download script and test from a clean environment.
• Using real employer or client data in public repositories. Create synthetic data that mimics the structure instead.
• Writing as if every reader is a statistician. Add a short plain-language summary of what the model does and why it matters.