Data Scientist

Data Scientists turn messy, real-world data into decisions and shipped products. A typical week mixes SQL queries on a warehouse (Snowflake, BigQuery, Redshift), exploratory analysis in Python notebooks, building and deploying ML models (forecasting, recommenders, fraud detection, churn, NLP), and translating findings for product, growth, or finance stakeholders. In India's tech hubs, the role spans pure analytics-leaning DS at Flipkart/Swiggy/PhonePe, applied ML at FAANG-India and Razorpay/Paytm, and research-heavy DS at Microsoft Research India and pharma/genomics labs.

• r/MachineLearningReddit
  Global ML subreddit; mandatory daily skim for paper releases, model launches, and industry discussion.
• r/datascienceReddit
  Career-focused subreddit; salary negotiation, interview reports, switch advice, and 'is X worth learning' discussions.
• Kaggle (India leaderboard + forums)website
  Competition platform with an active Indian user base; still the cleanest way to build a public DS portfolio in your first 2 years.
• MLSpace / MLOps Community DiscordDiscord
  Largest practitioner Slack/Discord for production-ML engineers; weekly online talks and a strong India presence.
• Two Sigma / Quant Group on LinkedIn (India chapters)LinkedIn
  Loose LinkedIn networks of Indian quants and DSes at financial firms; useful when transitioning into BFSI or quant DS.
• AI4Bharat / IndiaML mailing listwebsite
  IIT Madras-led research community focused on Indian-language NLP and Indic AI; the closest thing to a national DS research hub.
• Krish Naik / iNeuron DiscordDiscord
  Beginner-to-intermediate Indian DS Discord; useful for early career, study groups, and project review.

• Designing Machine Learning Systemsbook
  by Chip Huyen
  Best end-to-end ML systems book in print; covers everything Indian product DSes are now expected to own — feature stores, training pipelines, deployment, monitoring.
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlowbook
  by Aurelien Geron
  The standard practical-ML textbook; if you can implement every example end-to-end, you're at strong DS-2 level.
• Causal Inference: What Ifbook
  by Miguel Hernan, Jamie Robins
  Free PDF; the gold-standard intro to causal inference and the foundation under every well-run A/B test or observational study you'll do at work.
• Eugene Yan blogblog
  by Eugene Yan
  Most-cited applied-DS blog; his writing on recsys, evaluation, and stakeholder management is a working reference for Indian DSes at recommender-heavy companies (Flipkart, Myntra, Zomato).
• Lilian Weng blogblog
  by Lilian Weng
  OpenAI researcher's deep dives on agents, RLHF, and prompt engineering — the cleanest written explainers of frontier ML topics anywhere.
• Distill.pub archiveblog
  by various
  Now archived but timeless visual explainers of deep learning; a few hours here will sharpen your intuition more than weeks of tutorials.
• fast.ai 'Practical Deep Learning for Coders'blog
  by Jeremy Howard, Rachel Thomas
  Free top-down DL course; the fastest path from beginner Python to a deployed image/text model — heavily used in Indian DS bootcamps.
• Krish Naik YouTubeyoutube
  by Krish Naik
  Indian DS content creator covering end-to-end projects, MLOps, and interview prep — same role for DS that takeUforward plays for SDE.
• The Batch (DeepLearning.AI newsletter)newsletter
  by Andrew Ng
  Weekly summary of major ML news and papers — fastest way to stay current without doom-scrolling Twitter.
• Chip Huyen blogblog
  by Chip Huyen
  Companion to her book; her LLM and production-ML posts are required reading before any senior DS interview at a product company.

• Write and optimize SQL on Snowflake/BigQuery/Redshift to pull and shape training data, and to investigate metric anomalies flagged by stakeholders.
• Run exploratory analysis in Jupyter/VS Code with pandas/Polars — distributions, correlations, leakage checks, sanity plots before any modeling work.
• Train, tune, and evaluate models (gradient boosting, deep learning, recommenders, or LLM fine-tunes) — version with MLflow / Weights & Biases; check for fairness and slice-level performance.
• Design and analyze A/B tests with the product team — define guardrail metrics, pick sample sizes, write up causal interpretation including segment effects.
• Stand up and review production ML pipelines with engineering — feature stores, batch + online inference, monitoring for drift and model decay.
• Translate findings into 1-page memos and stakeholder presentations — for PMs, growth, finance, or leadership who do not read notebooks.