893: How to Jumpstart Your Data Career (by Applying Like a Scientist) — with Avery Smith

Episode Overview

• This episode provides a comprehensive roadmap for aspiring data professionals, starting with overcoming self-doubt and reframing past experience to fit a data-centric career path.
• It introduces two core frameworks for success: "Every Turtle Swims Past" (ETSP) for a structured learning path (Excel, Tableau, SQL, Python) and "Skills, Portfolio, Network" (SPN) for a holistic job-seeking strategy.
• The conversation emphasizes an "application-first" learning philosophy, advocating for hands-on projects over dry theory to build practical, job-ready skills.
• It details a scientific and proactive approach to the job hunt, including A/B testing resumes, networking strategically, and creating a unique portfolio that stands out to hiring managers.

Key Concepts

• Overcoming Mindset Blocks: The biggest initial hurdle for career changers is often self-doubt and perfectionism; the first step is to stop rejecting yourself before an employer can.
• Reframing Past Experience: Nearly any previous job involves data in some form. The key is to identify and articulate those experiences in a "data-y" way to demonstrate relevant skills.
• The ETSP Learning Ladder: A recommended four-step learning path for beginners—Excel, Tableau, SQL, and Python ("Every Turtle Swims Past")—designed to build the most in-demand skills sequentially.
• The SPN Career Framework: A three-pillar method for landing a job, positing that technical Skills are the baseline, but a strong Portfolio and an active Network are the true differentiators.
• Application-First Learning: A philosophy that prioritizes hands-on building and practical application ("getting your hands dirty") first, turning to theory only when necessary to solve a problem or deepen understanding.
• Scientific Job Application: A data-driven approach to the job search that involves tracking applications, A/B testing resume variations, and diversifying job platforms beyond just LinkedIn.
• Portfolio vs. GitHub: A curated, presentable portfolio is distinct from a raw GitHub repository. A portfolio must be designed to showcase skills effectively, especially for data analyst roles, not just store code.
• Advanced AI Research Tools: A discussion on the power and ROI of high-end AI tools like OpenAI's Deep Research, which can act as an AI agent to save significant time on complex research tasks.

Quotes

• At 0:06 - "If you're spending, like let's say two hours a day on average, like 14 hours a week, I think it's possible to be like 100% prepared like skill-wise in about 12 weeks." - Avery Smith providing a tangible timeline for becoming job-ready.
• At 3:08 - "I think the number one hiring manager that rejects you from jobs is yourself. Like a lot of the times, we are our own worst self-critics." - Avery Smith identifying self-doubt as a primary obstacle in the job search.
• At 6:01 - "You have this analogy, 'Every Turtle Swims Past'—ETSP—which stands for Excel, Tableau, SQL, and Python. And so that's your recommended strategic learning ladder." - Jon Krohn introducing Avery's core framework for learning data skills.
• At 19:58 - "But I also pay $200 a month for Deep Research from OpenAI, because that is extraordinary." - Jon Krohn introduces a high-end AI tool he uses for complex research tasks, highlighting its exceptional capability.
• At 24:19 - "What sets you apart is your ability to display how talented you are via a portfolio, and how lucky you get slash how many doors you can open through your network." - Avery Smith explains that skills are just the baseline; a portfolio and network are what truly differentiate a candidate in the job market.
• At 24:48 - "And the reason that's the case is it's just how humans work. Like we as humans, humans hire humans and you have to know humans." - Avery Smith offers a simple yet profound justification for why networking is a critical and non-negotiable part of securing a job.
• At 29:48 - "I just called them out and I was like, I built this project already. I web scraped all of your shot data... and you guys should hire me for an internship." - Avery Smith details his successful, direct approach to getting an internship with the Utah Jazz by creating a relevant project and using his network to showcase it.
• At 46:27 - "Let's get your hands dirty, and once it's dirty, you'll understand the theory a little bit more." - Avery Smith summarizing his "application-first" philosophy for learning data skills, where practical work precedes deep theoretical study.
• At 51:14 - "He was A/B testing his resume... he'd take one thing on his resume and he'd change it and maybe apply for 10 jobs. And then he'd change one thing on his resume and apply to 10 more jobs." - Avery Smith providing a real-world example of how to "apply like a scientist" by using a data-driven approach to the job hunt.
• At 52:33 - "It ends up you typically getting hundreds of applicants for a single role, and it is not a good tool for being able to easily sort from the hiring manager's perspective." - Jon Krohn explaining the ineffectiveness of mass-applying on platforms like LinkedIn.
• At 57:40 - "If you just do like normal, like the Titanic dataset... it's probably done a little too often." - Avery Smith advising against using common, overused datasets for portfolio projects because they fail to make a candidate stand out.
• At 59:18 - "I say something kind of controversial... GitHub is not a portfolio." - Avery Smith arguing that simply having a GitHub repository is insufficient; it needs to be curated into a proper, presentable portfolio to be effective.

Takeaways

• Start by addressing your mindset; overcoming the belief that you must be "perfect" is the first and most critical step to landing a data job.
• Audit your past jobs for data-related tasks and learn to articulate that experience on your resume to make your background an asset, not a liability.
• Follow a structured learning ladder like ETSP (Excel, Tableau, SQL, Python) to ensure you are building the most practical and in-demand skills first.
• Move beyond just acquiring skills by building a portfolio to showcase your talent and actively networking to open doors to opportunities.
• Prioritize hands-on projects to learn new tools and concepts, turning to theory when you encounter a roadblock rather than starting with it.
• Treat your job search as a data problem: track your applications, A/B test your resume, and diversify the platforms you use to find roles.
• Create a unique portfolio by avoiding overused datasets (like the Titanic dataset) and focusing on projects that solve interesting problems relevant to the jobs you want.