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Turn data into decisions. Build ML/AI models that drive product behaviour at scale.
Data scientists frame business problems as data problems, build models (statistical, ML, deep learning), and ship them into production. Day to day mixes coding, math/statistics, and business communication. The job is less about exotic algorithms and more about cleaning messy data, picking the right metric, and convincing non-technical stakeholders.
3 viable paths.
BTech (any branch) → MS Data Science → Job
Strongest signal. IIIT Bangalore, IIT Madras Online Degree, ISI Kolkata, IISc are top India options.
BSc/BTech → Specialisation via Coursera/Kaggle + Internships
Realistic. Top performers self-teach + win Kaggle medals + land applied research internships at startups.
Existing analyst/engineer → Internal transition
Most data scientists in industry start as data/business analysts and move sideways.
Wide bands — real salary depends on city, employer, performance. Pick the midpoint for planning.
Top MS programmes + product companies; service firms much less.
Senior DS or DS Manager at top product/fintech companies.
Principal DS, ML lead, head of DS.
Analyst → Data Scientist → Senior DS → Staff/Principal DS, OR DS Manager → Head of DS → VP Data.
Pros
Cons
Strong but bifurcating. Top tier (FAANG-equivalent) compensation continues to rise; mid-tier saturating as bootcamps flood the entry market.
Software Engineer
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