Research Data Scientist

• Conduct independent and collaborative research in Generative AI, LLMs, NLP, multimodal AI, machine learning, model evaluation, and AI data.
• Formulate research questions and translate complex AI/ML problems into structured research methodologies and experiments.
• Design, execute, and analyze experiments to evaluate and improve AI/ML models and solutions.
• Build analytical models, prototypes, and research pipelines using Python and relevant ML frameworks.
• Develop and implement LLM evaluation frameworks, benchmarks, datasets, and evaluation criteria.
• Evaluate models for accuracy, robustness, bias, hallucination, reasoning, relevance, response quality, and other performance dimensions.
• Conduct model benchmarking, error analysis, comparative analysis, and performance evaluation.
• Work on RAG, SFT, RLHF/DPO, prompt engineering, fine-tuning, embeddings, and LLM optimization, as applicable.
• Collect, clean, analyze, and interpret large and complex structured and unstructured datasets.
• Perform EDA, statistical analysis, hypothesis testing, significance testing, correlation analysis, sampling, and error analysis.
• Develop and evaluate datasets, sampling methodologies, taxonomies, annotation frameworks, data quality frameworks, and evaluation criteria.
• Analyze data quality and identify issues affecting model performance.
• Collaborate with annotation, data engineering, AI/ML, research, domain expert, and delivery teams.
• Contribute to research papers, technical reports, whitepapers, patents, benchmarks, internal publications, and other research outputs.
• Present research findings, analytical insights, and technical recommendations to senior technical stakeholders.
• Participate in client-facing technical discussions and presentations where required.
• Translate business requirements into AI/ML solutions and complex research concepts into actionable recommendations.

Requirements

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Computational Science, or a related discipline.
• 4–7 years of hands‑on research experience in AI/ML, Data Science, NLP, Generative AI, LLMs, or related areas.
• Strong demonstrated research experience with the ability to independently formulate research questions, design experiments, analyze results, and communicate findings.
• Demonstrated research track record through research publications, patents, conference presentations, open-source contributions, or significant AI/ML research projects.
• Strong proficiency in Python and SQL.
• Strong hands‑on experience with NumPy, Pandas, Scikit-learn, and preferably PyTorch/TensorFlow.
• Strong understanding of machine learning algorithms, statistics and experimentation, data analysis and feature engineering, model evaluation and performance metrics, hypothesis testing, and statistical inference.
• Hands‑on exposure to LLMs, NLP, Generative AI, and multimodal AI.
• Experience with one or more of RAG, LLM evaluation, prompt engineering, fine‑tuning, SFT, RLHF/DPO, embeddings, or model benchmarking.
• Experience working with large‑scale structured and unstructured datasets.
• Familiarity with Git and cloud platforms such as AWS, Azure, or GCP is desirable.
• Bachelor’s/Master’s degree from IITs, NITs, or other premier engineering/research institutions is strongly preferred.
• Candidates with publications in reputed conferences/journals and a strong academic/research profile will be preferred.

Core Competencies

Demonstrates expertise in Generative AI, LLMs, and NLP, with a strong ability to design and execute research methodologies, analyze complex datasets, and evaluate AI/ML models. Proficient in Python and relevant ML frameworks, with a solid understanding of machine learning algorithms and statistical analysis.

Highest-signal resume keywords

• Generative AI Research
• LLM Evaluation Frameworks
• Python Programming
• Machine Learning Algorithms
• Data Analysis and Feature Engineering

Hard Skills

• Machine Learning
• Natural Language Processing
• Statistical Analysis
• Model Evaluation
• Hypothesis Testing
• Data Cleaning
• Experiment Design
• Feature Engineering
• Error Analysis
• Benchmarking

Soft Skills

• Independent Research
• Collaboration
• Communication
• Analytical Thinking
• Problem Solving

Industry Keywords

• Artificial Intelligence
• Machine Learning
• Data Science
• Generative AI
• Multimodal AI
• Research Publications
• Technical Reports
• Conference Presentations
• Open‑source Contributions
• Research Methodologies

Tools & Technologies

• Python
• SQL
• NumPy
• Pandas
• Scikit-learn
• PyTorch
• TensorFlow
• Git
• AWS
• Azure