> Markdown version of [/jobs/ext/2554308-technical-lead](https://www.wearedevelopers.com/jobs/ext/2554308-technical-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Lead - **Company:** HCL America Inc. - **Location:** San Antonio, TX, United States - **Experience:** Expert - **Salary:** $155,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Data Cleansing, Data Transformation, Data Presentation, Python (Programming Language), Machine Learning, NumPy, Tableau (Software), Tokenization, Data Processing, Scripting, Large Language Models, Pandas, Natural Language Understanding - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7214e6c8eaec2d51 ## About the Role Strong Python skills for data manipulation using Pandas and NumPy. Experience with text preprocessing, data cleaning, tokenization, normalization, and data structuring. Working knowledge of ML models - specifically NLP/NLU/LLM concepts. Ability to create model governance documents and reports Other Requirements Strong analytical thinking, communication, and data storytelling skills. ## Description We are seeking a Conversational AI Data Analyst with a strong understanding of model performance metrics, ongoing monitoring, and model risk management. This role requires knowledge of NLU model performance measurement, LLM governance, and the reports and documentation needed to support model governance and risk management. The ideal candidate will have technical expertise in Python-based data analysis, model documentation, reporting, and Tableau-based visualization., Collect, clean, transform, and structure customer conversation scripts. Analyze conversational data by intent, entity, user segment, channel, fallback cases, and low-confidence predictions. Maintain and monitor intent classification machine Learning Models. Build python scripts to calculate model performance metrics and enhance and upgrade the model performance dashboards and reports. Document model performance metrics and prepare ongoing monitoring and periodic review materials for Model Risk Management. ## Related Videos - [JavaScript? No. Java Scripts! - Scripting with Java](https://www.wearedevelopers.com/videos/2094-javascript-no-java-scripts-scripting-with-java) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [The 13 Best Python Libraries for Developers in 2025](https://www.wearedevelopers.com/magazine/371-the-13-best-python-libraries-for-developers-in-2025) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)