Research Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct both independent and collaborative research in the fields of Generative AI, LLMs, NLP, multimodal AI, machine learning, model evaluation, and AI data.

• Formulate research inquiries and convert complex AI/ML challenges into structured research methodologies and experimental designs.

• Design, implement, and analyze experiments aimed at assessing and enhancing AI/ML models and solutions.

• Create analytical models, prototypes, and research pipelines utilizing Python along with relevant ML frameworks.

• Develop and execute LLM evaluation frameworks, benchmarks, datasets, and evaluation criteria.

• Assess models for accuracy, robustness, bias, hallucination, reasoning, relevance, response quality, and various other performance metrics.

• Conduct model benchmarking, error analysis, comparative studies, and performance evaluations.

• Engage in RAG, SFT, RLHF/DPO, prompt engineering, fine-tuning, embeddings, and LLM optimization where applicable.

• Gather, clean, analyze, and interpret extensive and intricate structured and unstructured datasets.

• Execute EDA, statistical analysis, hypothesis testing, significance testing, correlation analysis, sampling, and error analysis.

• Develop and assess datasets, sampling methodologies, taxonomies, annotation frameworks, data quality frameworks, and evaluation criteria.

• Analyze data quality and pinpoint issues impacting model performance.

• Collaborate with annotation, data engineering, AI/ML, research, domain experts, and delivery teams.

• Contribute to research papers, technical reports, whitepapers, patents, benchmarks, internal publications, and additional research outputs.

• Present research outcomes, analytical insights, and technical recommendations to senior technical stakeholders.

• Engage in client-facing technical discussions and presentations as necessary.

• Transform business requirements into AI/ML solutions and convert 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 field.

• 4–7 years of practical research experience in AI/ML, Data Science, NLP, Generative AI, LLMs, or associated disciplines.

• Proven research experience with the ability to independently develop research questions, design experiments, analyze results, and communicate findings effectively.

• Demonstrated research credentials through publications, patents, conference presentations, open-source contributions, or significant AI/ML research initiatives.

• Strong proficiency in Python and SQL.

• Extensive hands-on experience with NumPy, Pandas, Scikit-learn, and preferably PyTorch/TensorFlow.

• In-depth understanding of machine learning algorithms, statistical methods, experimentation, data analysis, feature engineering, model evaluation metrics, hypothesis testing, and statistical inference.

• Practical 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 handling 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 top-tier engineering/research institutions is highly preferred.

• Preference will be given to candidates with publications in reputable conferences/journals and a robust academic/research profile.

🏝️ Benefits

• Comprehensive health and wellness programs.

• Opportunities for professional development and continuous learning.

• Collaborative work environment with access to cutting-edge technology.

• Flexible work arrangements and work-life balance.