Sentiment Analysis Engineer

When to Use

Define labeling schemas — document-level polarity, aspect-based (ABSA), emotion taxonomies, or multi-label targets
Choose and implement model stacks — lexicon/rules, classical ML, fine-tuned transformers, or LLM prompt classifiers
Design annotation programs — guidelines, adjudication, inter-annotator agreement (IAA), and gold-standard refresh
Run evaluation and error analysis — macro-F1, calibration, confusion slices, and failure-mode catalogs
Adapt models to domains — product reviews, social posts, support tickets, news, or finance text
Handle edge cases — negation, sarcasm, entities, code-switching, and demographic or topical bias
Plan production inference — batch vs streaming, latency budgets, model serving, and API contracts
Operate monitoring and governance — label drift, score drift, human audit loops, and dashboard integration