Senior Specialist - Data Sciences

Job Summary

Seeking a Senior Specialist with 7 to 11 years of experience in Python and Data Science to develop advanced datadriven solutions that enhance business decisionmaking

Job Description

Develop and optimized NLP and machine learning models powering enterprise conversational AI and virtual assistant experiences focusing on intent classification entity recognition language understanding and accurate routing of naturallanguage requests

Build and maintain training validation and test datasets through utterance generation linguistic annotation data cleaning class balancing negative examples contextual variations and edgecase analysis to strengthen classifier performance and generalization

Apply computational linguistics and language engineering to design intententity definitions grammar rules regex patterns synonyms semantic variations and ambiguityresolution strategies for complex and overlapping conversational scenarios

Utilize Python and SQL across conversational AI workflows for data processing classifier validation model analysis automation troubleshooting and recurring NLP development activities

Evaluate and optimize intent classification performance using Linear SVM Precision Recall F1score Accuracy Confusion Matrices regression testing and falsepositivefalsenegative analysis to isolate model weaknesses and improve recognition quality

Analyze conversational data and model telemetry to uncover misclassified utterances intent overlap linguistic patterns dataquality gaps and performance trends translating findings into targeted trainingdata and model improvements

Apply prompt engineering techniques to design refine and evaluate prompts for Generative AI use cases focusing on contextual relevance response quality consistency and alignment with conversational requirements

Research emerging NLP Generative AI and LLM techniques including semantic approaches to evaluate opportunities for advancing intent recognition and enterprise conversational experiences

Collaborated crossfunctionally with ML engineers software engineers UX researchers product teams and business stakeholders across model development validation production implementation troubleshooting and Agile delivery while reporting classifier performance KPIs defects and optimization opportunities