Research Data Scientist

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Scope of the Role: 

We are looking for a highly skilled Research Data Scientist – GenAI/LLM to join our AI/LLM Delivery Unit and work 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.