Research Data Scientist – GenAI/LLM

Innodata (Nasdaq: INOD), a global data engineering company, specializes in enabling responsible AI advancement through data, evaluation frameworks, and expertise. They provide solutions for Generative AI and AI adopters, with a legacy of delivering high-quality data and outcomes.

Scope of the Role:

We are seeking a highly skilled Research Data Scientist – GenAI/LLM to join our AI/LLM Delivery Unit. This role focuses on research-driven AI/ML initiatives involving Generative AI, Large Language Models (LLMs), NLP, multimodal AI, model evaluation, and AI data.

The role combines strong research and analytical capabilities with hands-on AI/ML expertise, requiring the candidate to design experiments, develop evaluation methodologies, analyze complex datasets, build research prototypes, and translate research findings into practical AI/ML solutions.

The ideal candidate will have a strong research orientation, excellent statistical and analytical skills, and the ability to work collaboratively with researchers, data scientists, AI/ML engineers, domain experts, and client-facing teams.

What You’ll Own:

AI/ML & Generative AI Research:

• 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.
• Stay current with emerging research, methodologies, papers, and developments in GenAI, LLMs, NLP, multimodal models, and AI evaluation.

LLM & Model Evaluation:

• 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 areas such as RAG, SFT, RLHF/DPO, prompt engineering, fine-tuning, embeddings, and LLM optimization, as applicable.
• Identify model and data gaps and recommend improvements to enhance model performance and reliability.

Data Science & Statistical Research:

• 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 data-driven insights and identify patterns, trends, and relationships relevant to AI/ML research.
• Apply appropriate statistical and quantitative methodologies to validate research findings.

AI Data & Dataset Development:

• Develop and evaluate datasets, sampling methodologies, taxonomies, annotation frameworks, data quality frameworks, and evaluation criteria for AI/ML models.
• Analyze data quality and identify issues affecting model performance.
• Collaborate with annotation, data engineering, and AI/ML teams to improve AI training and evaluation data.
• Translate data and research findings into actionable recommendations for improving AI systems.

Research & Innovation:

• Contribute to research papers, technical reports, whitepapers, patents, benchmarks, internal publications, and other research outputs, where applicable.
• Identify opportunities to apply emerging research and technologies to real-world AI and data challenges.
• Explore new methodologies, models, datasets, and evaluation approaches to improve AI capabilities.
• Contribute to capability building and innovation within the AI/LLM practice.

Collaboration & Stakeholder Engagement:

• Work closely with researchers, data scientists, AI/ML engineers, data/annotation teams, domain experts, and delivery teams.
• Present research findings, analytical insights, and technical recommendations to senior technical stakeholders.
• Translate complex research and technical concepts into clear, actionable recommendations.
• Participate in client-facing technical discussions and presentations, translating business requirements into AI/ML solutions.

You’ll Thrive in This Role If You Have:

• A Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Computational Science, or a related discipline.
• A Bachelor’s/Master’s degree from IITs, NITs, or other premier engineering/research institutions is strongly preferred.
• 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.
• A demonstrated research track record through research publications, patents, conference presentations, open-source contributions, or significant AI/ML research projects.
• Candidates with publications in reputed conferences/journals and a strong academic/research profile will be preferred.
• 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.