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Senior Technical Product Owner
Career Insights for Natural Language Processing Engineer
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Based on Florida data
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What they do
A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
$107,803 / year median in Florida
Job Description
- Building a robust data foundation by developing and improving reusable datasets, integrating various data sources, and enhancing data quality and freshness.
- Driving business outcomes by improving revenue, member retention, operational efficiency, and member experience through the successful launch of products.
- Enhancing delivery by maximizing ROI on delivered features, ensuring accurate opportunity sizing, maintaining backlog throughput, ensuring sprint predictability, and optimizing time-to-delivery.
- Promoting adoption and scalability by increasing AI self-service uptake and reducing custom-dashboard and ad-hoc reporting requests.
- Proactively accountable for achieving business results through the conversion of disparate sources into scalable, AI-enabled data products. The right person has…
- Hands-on data mastery, proficient in writing SQL and Python to assess opportunities, prioritize actions, and measure ROI with a strong evidence-based approach.
- Ability to translate business goals from marketing, sales, operations, and member services into clear user stories and acceptance criteria, coupled with strong stakeholder management and engineering fluency.
- Expertise in data modeling, including the design of conceptual and logical models, and working with architects for physical builds.
- Ownership of outcomes by setting baselines, measuring real impact, and reporting quantified results beyond merely listing shipped features.
- Fluency in applied AI, with hands-on use of AI/LLM tools throughout the lifecycle and a focus on building self-service solutions to reduce custom reporting needs.