Hiring Data Scientists in India: Salary Benchmarks and Skill Requirements

Hiring data scientists in India is no longer just a “nice to have” – it is core to building competitive AI products and analytics capabilities, especially for startups and tech‑first enterprises.simplilearn+1​

Remote vs hybrid work India

Remote vs hybrid work in India has changed how companies plan AI/ML hiring, because top data talent now expects flexibility along with strong compensation. Offering remote or hybrid options lets employers hire beyond Bengaluru, Hyderabad, Pune, and NCR, often reducing costs while widening the candidate pool 2–4x.appliedaicourse+1​

Data scientist salary benchmarks in India

For 2025, most mid‑career data scientists in India earn in a broad band from about ₹17–18 LPA up to ₹30–35 LPA, depending on company type, city, and tech stack. Freshers and early‑career data scientists typically start around ₹6–10 LPA in service companies and can go into the low‑teens LPA range at product firms, while senior experts at large tech or late‑stage startups can reach ₹40–60 LPA+ including bonus and stock.6figr+2​

AI / ML & GenAI salary ranges

Machine learning engineers in India generally fall in the ₹6–10 LPA range for entry‑level roles, ₹10–20 LPA for mid‑level, and ₹20–50 LPA+ for senior engineers in top companies. Generative AI and advanced AI roles often command a premium, with many AI data scientists and GenAI engineers earning roughly ₹12–28 LPA on average, and senior specialists with 8–12 years of experience moving into the ₹35–55 LPA band.pwskills+3​

Core skills clients should budget for

To justify higher salary bands, employers usually look for a strong foundation in Python, SQL, statistics, experimentation, and hands‑on experience with libraries like pandas, NumPy, and scikit‑learn. For AI/ML and GenAI roles, in‑demand skills include deep learning frameworks (TensorFlow, PyTorch), cloud platforms, MLOps practices, and experience working with LLMs, transformers, and generative models in real products.coursera+3​

Practical hiring and budgeting tips

• Define the role clearly: Separate analytics‑heavy “data analyst / BI” work from true “data scientist / ML engineer” responsibilities so salary offers stay consistent with expectations.​
• Use tiered bands: Create separate bands for junior, mid‑level, and senior AI/ML roles, and adjust for remote vs metro locations rather than negotiating from scratch for every candidate.

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