NLP Data Scientist

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A NLP Data Scientist is a data scientist who specializes in text data modeling (correlational and causal modeling) and performs GenAI text data science tasks (e.g., discover insights with GenAI text models).

References

2024

  • Bard
    • 1. Text Data Scientist: This is the foundation of the role. They possess the core skills of a traditional data scientist, but focus on analyzing textual data rather than other data types like numerical or image data. This includes tasks like:
      • Data acquisition and pre-processing: Collecting, cleaning, and preparing text data for analysis.
      • Exploratory data analysis (EDA): Examining the data to understand its characteristics and identify potential patterns.
      • Modeling and analysis: Building and applying statistical or machine learning models to extract insights from the text data.
      • Communication and visualization: Presenting results and insights clearly and concisely.
    • 2. GenAI: This is the differentiating factor for this role. A GenAI Text-Data Data Scientist uses generative AI techniques, which involve training AI models to generate new data or content based on existing data. This opens up new possibilities for text analysis, such as:
      • Generating synthetic text data: Creating realistic anonymized data for training or testing purposes.
      • Augmenting existing data: Adding variations or perturbations to existing data to improve the robustness of models.
      • Automating data annotation: Using AI to label or classify text data, reducing manual effort automatically.
      • Discovering insights with GenAI text models: Leveraging the unique capabilities of GenAI models to uncover hidden patterns and relationships in text data.
    • 3. GenAI Text Data Science Tasks: This refers to the specific tasks that a GenAI Text-Data Data Scientist might perform. Examples include:
      • Developing and optimizing GenAI models for text analysis: Choosing the right model architecture, training data, and evaluation metrics.
      • Interpreting and explaining the results of GenAI models: Understanding how the models arrived at their insights and ensuring their reliability.
      • Integrating GenAI models with traditional data science workflows: Combining the power of GenAI with other data analysis techniques.