Data Scientist(Intern)

Role & responsibilities

Build and evaluate machine learning models for real-world systems in defence, energy, and critical-infrastructure settings. You will:

• Build working baselines in one core area Computer Vision, NLP, Generative AI, Time Series, or classical ML and iterate toward production-grade quality.
• Investigate where and why models fail: design controlled experiments, measure failure modes honestly (including negative results), and report what the evidence actually shows.
• Source, justify, and work with appropriate datasets, and establish sound evaluation methodology — baselines, metrics, ablations — rather than one-off results.
• Turn research findings into reliable, deployable components that enterprises and government partners can own and trust.
• Write up your work clearly — hypothesis, method, results, limitations, next steps — for both technical and non-technical readers.

Preferred candidate profile

Must-have

• Strong Python; able to write, debug, and reason about code independently.
• Solid ML fundamentals — not just calling APIs, but understanding how the algorithms work, what they assume, and where they break.
• Genuine measurement discipline: you evaluate with proper baselines and metrics, and report results honestly even when they are unflattering.
• Real depth in at least one area (CV / NLP / GenAI / Time Series / classical ML) rather than shallow breadth across all.
• Curiosity and the instinct to trace unexpected behaviour back to its root cause.

Nice-to-have

• Exposure to a relevant domain: security / OT-ICS, earth observation, industrial or sensor time series, or anomaly detection.
• Practical MLOps (experiment tracking, reproducibility, containerization), or RAG / agentic systems experience.
• Research experience — papers, ablation studies, or rigorous benchmarking.

Perks and benefits

• 6–7 month internship working on a real, research-level product — not toy tasks.
• Monthly stipend: 10,000–15,000.
• Path to full-time conversion based on performance, at 6–7 LPA.
• Direct mentorship from the research team.
• Hands-on work in sovereign / critical-infrastructure AI across defence, energy, and critical infrastructure.
• Internship completion / experience certificate.