893: How to Jumpstart Your Data Career (by Applying Like a Scientist), with Avery Smith
🎯 Summary
Podcast Episode Summary: 893: How to Jumpstart Your Data Career (by Applying Like a Scientist), with Avery Smith
This 77-minute episode of the Super Data Science Podcast features Avery Smith, founder of Data Career Jumpstart, providing actionable advice for professionals—especially career changers—looking to secure their first role in data analytics or data science. The discussion blends essential mindset shifts with a practical, prioritized technical learning roadmap.
1. Focus Area
The primary focus is on Career Transition and Job Acquisition in Data Roles (Data Analyst/Data Scientist). Key themes include overcoming self-doubt, leveraging existing professional experience, strategic skill acquisition, and practical job application strategies.
2. Key Technical Insights
- The “Every Turtle Swims Past” (ETSP) Learning Ladder: Avery advocates for a strategic learning sequence prioritizing ease of learning alongside market demand to secure a job quickly: Excel $\rightarrow$ Tableau $\rightarrow$ SQL $\rightarrow$ Python. The goal is to get paid experience as soon as possible, as on-the-job learning is the most relevant.
- LLMs as Productivity Tools, Not Replacements: While AI tools like ChatGPT and Claude are excellent for generating 60-70% of initial code, beginners must still learn the fundamentals (like programming logic) to identify and correct AI errors.
- The Value of Internal Pivots: The safest and often most effective career transition involves leveraging existing company trust to gradually transition into a data role internally, even if starting part-time.
3. Business/Investment Angle
- Mindset as a Hiring Barrier: Self-doubt is identified as the number one internal barrier to landing a data job; overcoming the need for perceived “perfection” is crucial for applying confidently.
- Leveraging Anti-Goals: Motivation is often more effectively sparked by the pain of the current situation (e.g., not wanting to risk life for low pay, as in Avery’s chemical engineering past) than by abstract aspirational goals.
- The Speed of Data Evolution: The industry moves so fast that mastery is impossible; comfort with problem-solving and figuring things out is more valuable than deep specialization in any single, rapidly changing tool.
4. Notable Companies/People
- Avery Smith: Founder of Data Career Jumpstart, host of the Data Career Podcast, and principal at Snow Data Science and Analytics (client: Utah Jazz).
- John Cron (Host): Host of the Super Data Science Podcast.
- Ben Todd: Mentioned in reference to his work on the 80,000 Hours career advice platform.
- Oliver Burkeman: Author of 4,000 Weeks, discussed in relation to the finite nature of time and work.
5. Future Implications
The industry is rapidly integrating LLMs into daily workflows. Future data professionals must become adept at leveraging these tools effectively (e.g., using Deep Research modes for complex queries) to maximize efficiency, rather than viewing them as a threat to their roles.
6. Target Audience
This episode is most valuable for Career Changers, Aspiring Data Analysts, and Junior Data Scientists who are struggling with where to start their technical learning journey or how to approach the job market with non-traditional backgrounds.
Comprehensive Summary
Episode 893 centers on the practical and psychological hurdles of breaking into the data industry, featuring Avery Smith, a prominent figure in data career coaching. The narrative begins with Avery’s dramatic personal pivot from a dangerous chemical lab technician role (highlighted by a near-miss with hydrofluoric acid) to a data scientist position, driven by the anti-goal of avoiding physical risk and seeking better work conditions.
A major theme is mindset. Avery stresses that self-doubt is the primary obstacle, urging listeners to stop waiting for external permission (degrees, managers) and to reframe past experiences as assets. He champions the idea that in the fast-moving data field, being comfortable with not knowing everything is key, encouraging listeners to apply for roles even if they only meet 50% of the criteria.
Technically, Avery introduces his Every Turtle Swims Past (ETSP) Learning Ladder (Excel, Tableau, SQL, Python). This sequence is deliberately structured based on a quadrant of demand vs. ease of learning. Beginners should start with the easiest, highly demanded tools (Excel, Tableau) to gain quick wins and build momentum, progressing to more complex programming (SQL, then Python). This strategy is designed to get candidates into their first paid data role as fast as possible, where the most valuable learning occurs.
The conversation also addresses modern tools, specifically Large Language Models (LLMs). Both Avery and the host agree that LLMs are indispensable coding partners, but caution that foundational knowledge is necessary to validate and correct AI-generated output. They discuss preferences between tools like Claude and ChatGPT, noting the utility of advanced features like ChatGPT’s “Deep Research” for sourcing and synthesizing complex information.
Finally, Avery highlights the strategic advantage of internal career pivots, noting that existing trust within a company significantly lowers the hiring risk associated with external career changers. The episode provides a comprehensive framework for aspiring data professionals, focusing equally on psychological readiness and a pragmatic, prioritized technical skill acquisition path.
🏢 Companies Mentioned
đź’¬ Key Insights
"So my whole philosophy is, let's get you into a data job as quickly as possible. Let's get your foot in the door as soon as possible because the learning, the best learning that happens is on-the-job learning."
"It is because it's instead of just stream-of-consciousness outputting tokens immediately, it's reflecting on its progress. And so it's constantly iterating on its outputs and on its chain of thought."
"My whole philosophy is get your foot in the door as quickly as possible. And then in terms of, okay, well, how do we get your foot in the door as quickly as possible? Well, you need to focus on the skills that are in demand first off, but then also easy—the difficulty plays a role."
"I also pay $200 a month for Deep Research from OpenAI because that is extraordinary when I need it. It's really, really good when you have a more complex question, you want an agent to crawl the web, dig up answers on up-to-date information, and provide you with a thorough report."
"I think AI can get me usually 60, maybe 70% of the way there. But there is going to be a lot of times where AI is going to be wrong. If you don't know how to program, you're not going to know where it's wrong a lot of the time."
"I think people look at it incorrectly and think that it's going to replace people. I don't think it's going to replace professionals. I think it just enables professionals to work smarter."