Job
Description
Desirable Candidate We are looking for a highly experienced NLP Analyst with deep expertise in linguistic data analysis, annotation design, and production-scale NLP model evaluation. This role requires a blend of linguistic acumen, analytical rigor, and real-world application experience. You will drive the design and execution of NLP initiatives across diverse domains and guide cross-functional teams on best practices in language data handling and annotation quality. Key Responsibilities Manage large-scale text annotation and labeling pipelines for supervised and semi-supervised learning. Conduct advanced linguistic analysis of unstructured content (e.g., clinical notes, legal contracts, customer communications, claim documents) to identify patterns, gaps, and modeling opportunities. Define and enforce annotation schemas and QA protocols for complex NLP tasks (e.g., NER, relation extraction, coreference resolution, sentiment/intent classification). Evaluate and improve the performance of NLP models through rigorous error analysis and metric-driven feedback loops. Collaborate with ML/NLP engineers, data scientists, and domain experts to build robust NLP pipelines that scale across use cases. Lead internal research efforts on emerging NLP methodologies, including LLM prompt engineering, hybrid rule-learning approaches, and few-shot learning. Provide mentorship to junior analysts and contribute to developing internal NLP knowledge repositories and annotation standards. Required Qualifications Masters in computational Linguistics, NLP, Data Science, Computer Science, or a related field. 5+ years of professional experience in NLP, with a strong track record of hands-on work in data annotation, language model evaluation, and NLP pipeline development. Expertise in Python and key NLP libraries (spaCy, NLTK, Scikit-learn, Hugging Face Transformers, etc.). Advanced proficiency in building and managing annotation workflows using tools like Prodigy, doccano, Brat, or in-house platforms. Deep understanding of linguistic structures (syntax, semantics, pragmatics) and their application to real-world NLP challenges. Experience evaluating ML/NLP models using metrics like F1, ROUGE, BLEU, precision/recall, and embedding-based similarity. Solid grasp of vectorization methods (TF-IDF, embeddings, transformer-based encodings) and modern language models (e.g., BERT, GPT, LLaMA). Preferred Qualifications Experience with domain-specific NLP (e.g., clinical/biomedical, legal, fintech). Knowledge of knowledge graph construction, relation extraction, and entity linking. Experience integrating structured/unstructured data for downstream AI/ML applications. Familiarity with prompt engineering for LLMs and tuning foundation models. Strong data querying and visualization skills (SQL, pandas, seaborn, Power BI/Tableau).
