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Use-Case: Identifying Skill Gaps

LLM

GPT 4o mini

Platform

Perplexity AI

Date

14-Nov-2024

## Prompt

Review my resume.

Can you identify any skills gaps that I may have and which I may wish to close to meet my career aspirations.

I want to continue working at the intersection of data and mission-driven organisations.

I am very eager to leverage LLMs and AI to both "surface" (identify new data), streamline insight generation, and help with analysis.

## Contextual Data

Resume (strip of non-data items like photos, attach as markdown)

## Output

### Summary of your request: You are seeking feedback on your resume to identify potential skills gaps, particularly in the context of working at the intersection of data and mission-driven organizations. You are also eager to leverage Large Language Models (LLMs) and AI to surface new data, streamline insight generation, and support analysis.

Current Strengths:

Your resume reflects a strong foundation in communications, storytelling, and data visualization, particularly within mission-driven sectors like sustainability and impact investing. These are key strengths that align well with your goal of continuing to work at the intersection of data and purpose-driven organizations. Additionally, your experience with environmental metrics, strategic communications, and collaboration with data scientists is highly relevant as you transition into more data-centric roles.

Identified Skills Gaps:

  1. Advanced Technical Skills in AI & LLMs:
  2. While you list AI & LLMs as a skill, your experience in this area is not deeply detailed. Given your interest in leveraging these technologies for data analysis and insight generation, you may want to focus on building more advanced technical proficiency.

  3. Suggested actions:

    • Gain hands-on experience with LLM frameworks like OpenAI’s GPT or Hugging Face models.
    • Work on projects that involve fine-tuning LLMs for specific tasks like text classification, summarization, or generating insights from large datasets.
    • Explore how LLMs can be integrated into workflows for mission-driven organizations (e.g., automating report generation or identifying patterns in environmental data).

  4. Data Science & Analytics:

  5. Your proficiency in Python is listed as beginner level, which might limit your ability to fully leverage AI and LLMs for data analysis. To meet your aspirations, improving your Python skills—especially for data science tasks—would be crucial.

  6. Suggested actions:

    • Complete the Python for Data Science course and focus on libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow.
    • Dive deeper into machine learning algorithms that can complement LLMs, such as clustering or regression techniques for generating insights from structured data.
    • Consider working on real-world projects that involve analyzing large datasets related to sustainability or impact investing.

  7. Data Engineering & Automation:

  8. While you have strong skills in data visualization and environmental metrics, there’s no mention of experience with data engineering tools (e.g., SQL, ETL pipelines) or automation frameworks that could help streamline insight generation from raw data.

  9. Suggested actions:

    • Learn SQL for querying databases efficiently.
    • Explore tools like Apache Airflow or Prefect for orchestrating automated workflows.
    • Familiarize yourself with cloud platforms (AWS, Google Cloud) that offer scalable solutions for managing large datasets.

  10. AI Ethics & Responsible AI:

  11. Since you are working with mission-driven organizations focused on sustainability and impact investing, understanding the ethical implications of AI could be a valuable asset. This could help differentiate you when working with organizations concerned about the responsible use of AI technologies.

  12. Suggested actions:

    • Take courses on AI ethics or responsible AI development.
    • Engage with the broader conversation around bias in AI models and how it affects decision-making in areas like sustainability.

  13. Project Management & Leadership in Tech-Driven Initiatives:

  14. While you have solid experience managing communications projects, there’s no mention of leading or managing tech-driven initiatives involving data science or AI. As you move into roles where AI plays a larger part in decision-making processes, project management skills tailored to technical teams could be beneficial.
  15. Suggested actions:
    • Consider certifications such as Agile or Scrum that focus on managing cross-functional teams (especially those involving developers and data scientists).
    • Lead small-scale AI-related projects within your current organization to gain practical experience.

Suggested Learning Pathways:

  • Short-term goals (3-6 months):
  • Complete ongoing certifications (Python for Data Science, Data Visualization with Python, AI & Machine Learning Fundamentals).
  • Build small projects using LLMs to gain hands-on experience (e.g., automating content generation or analyzing text-based datasets).

  • Improve Python proficiency by focusing on intermediate-to-advanced topics relevant to data science.

  • Long-term goals (6-12 months):

  • Develop expertise in integrating LLMs into workflows specific to mission-driven organizations (e.g., environmental monitoring or social impact assessment).
  • Gain proficiency in SQL and cloud platforms to handle larger datasets efficiently.
  • Explore leadership roles where you can manage cross-disciplinary teams working on tech-driven solutions.

Conclusion:

Your resume demonstrates strong communication skills and a growing interest in AI and LLMs; however, there are gaps in advanced technical skills related to AI implementation and data science that may limit your ability to fully leverage these technologies within mission-driven organizations. By focusing on improving your Python proficiency, gaining hands-on experience with LLMs, and understanding the ethical dimensions of AI use cases, you will be better positioned to meet your career aspirations at the intersection of data and impact-driven work.