Research Data Scientist at Innodata Inc.
United States
$160k - $185k
<div class="content-intro"><p>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.</p></div><p><strong>Scope of the Role: </strong></p> <p></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>What You’ll Own:</strong></p> <p></p> <p>AI/ML & Generative AI Research:</p> <ul> <li>Conduct independent and collaborative research in Generative AI, LLMs, NLP, multimodal AI, machine learning, model evaluation, and AI data.</li> <li>Formulate research questions and translate complex AI/ML problems into structured research methodologies and experiments.</li> <li>Design, execute, and analyze experiments to evaluate and improve AI/ML models and solutions.</li> <li>Build analytical models, prototypes, and research pipelines using Python and relevant ML frameworks.</li> <li>Stay current with emerging research, methodologies, papers, and developments in GenAI, LLMs, NLP, multimodal models, and AI evaluation.</li> </ul> <p></p> <p></p> <p>LLM & Model Evaluation:</p> <ul> <li>Develop and implement LLM evaluation frameworks, benchmarks, datasets, and evaluation criteria.</li> <li>Evaluate models for accuracy, robustness, bias, hallucination, reasoning, relevance, response quality, and other performance dimensions.</li> <li>Conduct model benchmarking, error analysis, comparative analysis, and performance evaluation.</li> <li>Work on areas such as RAG, SFT, RLHF/DPO, prompt engineering, fine-tuning, embeddings, and LLM optimization, as applicable.</li> <li>Identify model and data gaps and recommend improvements to enhance model performance and reliability.</li> </ul> <p></p> <p></p> <p><em>Data Science & Statistical Research:</em></p> <ul> <li>Collect, clean, analyze, and interpret large and complex structured and unstructured datasets.</li> <li>Perform EDA, statistical analysis, hypothesis testing, significance testing, correlation analysis, sampling, and error analysis.</li> <li>Develop data-driven insights and identify patterns, trends, and relationships relevant to AI/ML research.</li> <li>Apply appropriate statistical and quantitative methodologies to validate research findings</li> </ul> <p></p> <p></p> <p><em>AI Data & Dataset Development:</em></p> <ul> <li>Develop and evaluate datasets, sampling methodologies, taxonomies, annotation frameworks, data quality frameworks, and evaluation criteria for AI/ML models.</li> <li>Analyze data quality and identify issues affecting model performance.</li> <li>Collaborate with annotation, data engineering, and AI/ML teams to improve AI training and evaluation data.</li> <li>Translate data and research findings into actionable recommendations for improving AI system</li> </ul> <p></p> <p><em>Research & Innovation:</em></p> <ul> <li>Contribute to research papers, technical reports, whitepapers, patents, benchmarks, internal publications, and other research outputs, where applicable.</li> <li>Identify opportunities to apply emerging research and technologies to real-world AI and data challenges.</li> <li>Explore new methodologies, models, datasets, and evaluation approaches to improve AI capabilities.</li> <li>Contribute to capability building and innovation within the AI/LLM practice</li> </ul> <p></p> <p><em>Collaboration & Stakeholder Engagement:</em></p> <ul> <li>Work closely with researchers, data scientists, AI/ML engineers, data/annotation teams, domain experts, and delivery teams.</li> <li>Present research findings, analytical insights, and technical recommendations to senior technical stakeholders.</li> <li>Translate complex research and technical concepts into clear, actionable recommendations.</li> <li>Where required, participate in client-facing technical discussions and presentations and help translate business requirements into AI/ML solutions.</li> </ul> <p><strong>You’ll Thrive in This Role If You Have:</strong></p> <ul> <li>Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Computational Science, or a related discipline.</li> <li>Bachelor’s/Master’s degree from IITs, NITs, or other premier engineering/research institutions is strongly preferred.</li> <li>4–7 years of hands-on research experience in AI/ML, Data Science, NLP, Generative AI, LLMs, or related areas.</li> <li>Strong demonstrated research experience with the ability to independently formulate research questions, design experiments, analyze results, and communicate findings.</li> <li>Demonstrated research track record through research publications, patents, conference presentations, open-source contributions, or significant AI/ML research projects.</li> <li>Candidates with publications in reputed conferences/journals and a strong academic/research profile will be preferred</li> <li>Strong proficiency in Python and SQL.</li> <li>Strong hands-on experience with NumPy, Pandas, Scikit-learn, and preferably PyTorch/TensorFlow.</li> <li style="font-style: italic;"><em>Strong understanding of:</em> <ul> <li style="font-style: italic;">Machine learning algorithms</li> <li style="font-style: italic;">Statistics and experimentation</li> <li style="font-style: italic;">Data analysis and feature engineering</li> <li style="font-style: italic;">Model evaluation and performance metrics</li> <li>Hypothesis testing and statistical inference</li> </ul> </li> <li>Hands-on exposure to LLMs, NLP, Generative AI, and multimodal AI.</li> <li>Experience with one or more of RAG, LLM evaluation, prompt engineering, fine-tuning, SFT, RLHF/DPO, embeddings, or model benchmarking.</li> <li>Experience working with large-scale structured and unstructured datasets.</li> <li>Familiarity with Git and cloud platforms such as AWS, Azure, or GCP is desirable.</li> </ul> <p></p> <ul> <li> </li> </ul> <p></p> <p></p> <p><em>The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.</em></p> <p> </p><div class="content-conclusion"><p></p> <p class="x_elementToProof"><em>Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at </em><a href="https://consumer.ftc.gov/articles/job-scams." target="_blank">https://consumer.ftc.gov/articles/job-scams.</a><em> </em></p> <p class="x_elementToProof"><em>If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at </em><a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a><em> and consider reporting it to the FTC at </em><a href="http://reportfraud.ftc.gov/" target="_blank">ReportFraud.ftc.gov</a><em>.</em></p> <p></p></div>
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