Episode 1: In Conversation with Kaitlin Bustos

This first episode of CareerQuest, hosted by Dataquest’s CEO Celeste Grupman and Womxn in Data Science CEO Kishawna Peck, features Kaitlin Bustos, a Data Career Coach, LinkedIn Top Data Science Voice, and one of North America’s Top 10 Young AI Role Models. Kaitlin shares her inspiring journey from a psychology background to thriving in the data science industry, covering life sciences, national defense, and more. The session offers practical advice, personal stories, and motivational insights for aspiring data professionals.

Key Takeaways:

1. Kaitlin’s Career Journey:

   • Transitioned from psychology to data science, leveraging her background and learning through a structured data science program.
   • Highlighted the importance of persistence, accountability, and having a clear plan when learning and growing in the field.

2. Learning Strategies:

   • Structure your learning path with accountability mechanisms like schedules.
   • Focus on a few high-quality, relevant portfolio projects tailored to your desired roles.
   • Use tools like ChatGPT for mock interviews and job-specific preparation.

3. Breaking into Data Science:

   • Start with tutoring or freelance opportunities to gain experience.
   • Align your unique skills or domain knowledge (e.g., psychology) with relevant roles.
   • Customize applications for each job and research companies building their data foundations.

4. Community and Mentorship:

   • Engaging in communities is essential for support, inspiration, and networking.
   • Mentorship can offer career guidance, emotional support, and opportunities.

5. Technical Interviews:

   • Communicate your thought process, use pseudocode, and always think out loud.

   • Don’t jump straight to coding; focus on problem-solving and structured approaches.

6. Navigating the Job Market:

   • Tailor your applications to specific roles and avoid a "spray-and-pray" approach.
   • Look beyond big tech companies; many industries are expanding their use of data and AI.

7. Overcoming Challenges:

   • Embrace the ambiguity and longer timelines of industry projects compared to academia.
   • Strengthen your foundation in key math concepts like matrix algebra when needed.