Research Data Scientist
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Job Description
Research Data Scientist advancing Innodata’s data engineering and AI services through GenAI, LLM, NLP, and multimodal research. Building evaluation frameworks, datasets, prototypes, and statistical insights.
Responsibilities:
- 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.
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