Posted 15h ago
AI Data Scientist
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What this role pays, in context
This listing pays 399% above the median for Data Scientist roles in India, which sits at ₹5.1L a year.
25th pct ₹3.4L75th pct ₹5.8L
Median of 14 other live Data Scientist listings in India on this board that state pay. Roles that do not disclose a salary are excluded rather than counted as zero.
We are looking for a hybrid Data Scientist cum AI Scientist who can move fluidly between classical data science (statistical modeling, experimentation, analytics) and applied AI/ML research (deep learning, LLMs, generative AI). You will own the full lifecycle of data-driven and AI-driven initiatives — from problem framing and data exploration, to model development, to production deployment — partnering closely with engineering, product, and business stakeholders.
Responsibilities
- •Design, build, and validate statistical and machine learning models to solve business problems (forecasting, classification, recommendation, anomaly detection, etc.)
- •Research, prototype, and fine-tune AI/ML models, including deep learning architectures, NLP, computer vision, and generative AI / LLM-based systems
- •Own the end-to-end ML lifecycle: data collection and cleaning, feature engineering, model training, evaluation, deployment, and monitoring
- •Translate ambiguous business problems into well-defined data science and AI research questions
- •Conduct exploratory data analysis and build dashboards / reports to communicate insights to non-technical stakeholders
- •Design and analyze A/B tests and other experiments to measure impact of models and product changes
- •Stay current with the latest AI research (papers, frameworks, tools) and evaluate applicability to the business
- •Leverage AI/ML to streamline internal workflows and deploy LLM-driven AI Agents to manage low to high complexity decision-making tasks
- •Document methodology, assumptions, and model limitations; ensure responsible and explainable AI practices
- •Mentor junior data scientists/analysts and contribute to best practices, code quality, and reproducibility standards
Requirements
- •Hands-on experience in data science, machine learning, or applied AI roles
- •Strong programming skills in Python (pandas, NumPy, scikit-learn) and SQL
- •Practical experience with deep learning frameworks such as PyTorch or TensorFlow
- •Solid foundation in statistics, probability, and experimental design (A/B testing, causal inference)
- •Experience working with LLMs, transformer architectures, embeddings, or generative AI tools (e.g., OpenAI, Anthropic, Hugging Face)
- •Familiarity with MLOps practices: model versioning, CI/CD for ML, model monitoring (e.g., MLflow, Kubeflow, or similar)
- •Experience with cloud platforms (AWS, GCP, or Azure) for training and deploying models
- •Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
- •Comprehensive proficiency in LangChain, LangGraph, and modern multi-agent architecture frameworks