Job Description

LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets. TechStack: SQL ,Python, LLM Techniques, NLP Models, Tableau, Excel Roles and Responsibilities Model Development and Deployment Develop NLP models for topic modeling and sentiment analysis using OpenAI and other machine learning techniques. Use Large language model (LLM) techniques to enhance the accuracy and relevance of verbatim analysis. Aim for model accuracy exceeding 85% by refining and iterating on the models. Deploy model into production. Data Processing and Pipeline Management Set up and maintain data pipelines using Databricks and other existing tools to ensure seamless data flow and storage. Clean and prepare datasets, ensuring data quality and consistency for analysis. Dashboard and Reporting Create interactive Tableau dashboards and Excel reports to visualize key insights and trends for stakeholders. Provide regular updates and reports on model outputs, data trends, and survey insights. Stakeholder Communication Meet regularly with stakeholders to gather requirements, provide updates, and discuss insights. Document all work, including model development processes, data pipeline setups, and analysis results, on Confluence for transparency and collaboration. Ad-Hoc Analysis and Insights Generation Conduct ad-hoc analysis as requested by the CAHPS team to address specific questions or emerging patterns in the data. Generate insights and recommendations based on survey data to support strategic decision-making. Trend Analysis for Long-Term Insights Track changes in sentiment and topic prevalence over time to identify shifts in patient experience. Highlight any emerging issues or improvements based on trends over several months. Benchmarking Compare monthly survey results against historical data to establish benchmarks and identify outliers. Root Cause Analysis Identify root causes for areas of low satisfaction by linking structured and unstructured survey responses. LatentView Analytics

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