Dr. Jon Krohn893: How to Jumpstart Your Data Career (by Applying Like a Scientist) — with Avery Smith
synopsis
Avery Smith, a former chemical lab technician who now trains career changers through his Data Career Jumpstart bootcamp and Data Career Podcast, argues that the quickest way into data is to get a foot in the door rather than master everything first. He would learn the easiest in-demand tools, show them in a portfolio built on real interests, network as hard as you study, and treat the job hunt itself as a data problem. He reckons someone studying two hours a day can be skill-ready in about twelve weeks.
Mindset. Smith says the person most likely to reject a candidate is the candidate. A student of his with no technical degree got hired partly by framing earlier work as data work. Since data is too vast and fast-moving to master, he tells people to apply once they meet roughly half the requirements. The host adds research he has read that some groups, women for example, are less likely to apply without meeting every criterion.
His own pivot. A hydrofluoric-acid splash on an ungloved hand, at minimum wage, convinced Smith to leave the lab. His small employer (under 15 people) had a data scientist with a window office, and it let Smith move into a junior analyst role at 25%, then 50%, then 100% of his time. He calls an internal pivot the best option for career changers because the employer already knows and trusts them, though he doubts it would have worked at ExxonMobil, where he later worked. He describes his motivation as an anti-goal: escaping where he was. He notes that a working life runs 80,000 to 90,000 hours.
The learning ladder. "Every Turtle Swims Past" means Excel, Tableau, SQL, Python, ranked by demand against ease. Excel is familiar, Tableau is drag-and-drop, SQL is the most in demand and easier than Python, and Python comes last. The host says he seldom writes code character by character; Smith says AI gets him 60–70% of the way, but you need to program to catch its errors. The host uses Claude day to day and pays $200 a month for OpenAI's deep research.
Skills, portfolio, network. Smith weights his SPN method equally, and says networking may matter more than skills because "humans hire humans" from a thousand applicants. The host says nearly all his own opportunities came from meeting people in person. Under "resumes talk, projects walk", Smith says a project turns an interview into questions about your own work. After three or four rejections he won a Utah Jazz internship with no interview by posting a project analysing their shot data on LinkedIn. His minimum for analysts is one SQL and one data-viz project, chosen for interest so it gets finished. Personal projects are motivating, he says, but their data can be hard to collect, as with phone screen time. Even a delivery driver A/B-testing gate-access texts earned a strong résumé line. He would avoid Titanic, Iris, MNIST and Google's bike-share capstone. A GitHub profile, he argues, is a poor portfolio unless curated, since recruiters give it 10 seconds before granting 60.
Applying like a scientist. Smith has students apply from week four to six, and says many are hired within 100 days. About 15% of data jobs he analysed were fully remote, far fewer than the share of people who want remote work, so remote searches take longer. He borrows A/B-testing of résumés from a past guest and requires a job tracker. When he posted a dummy analyst job on LinkedIn saying "do not apply", it drew hundreds of applicants within 24 hours, and he found its candidate ranking made no sense. The host agrees that LinkedIn applications mostly vanish into a black hole.
- Career changers reject themselves before employers do. Smith says "the number one hiring manager that rejects you from jobs is yourself". Data is everywhere, so past roles can be framed as data experience; at around 50% of a job's requirements, he says, "just apply". The host adds research he has read that women are less likely than men to apply without meeting every criterion.
- An internal pivot is the lowest-risk route into data. After a near-miss with hydrofluoric acid as a lab technician, Smith moved into a junior analyst role at the same company, at 25%, then 50%, then 100% of his time. He says employers who already know and trust you see less risk. He credits the firm's size, under 15 people, and doubts it would have worked at ExxonMobil, where he later worked.
- Learn data tools in order of demand and ease. His "Every Turtle Swims Past" ladder (Excel, Tableau, SQL, Python) starts with what is in demand and easy. SQL is "probably the most in demand", and Python comes last for its steep learning curve. The aim is to get hired fast, since on-the-job learning is best. He says AI gets him 60–70% of the way on code, but you must know programming to see where it is wrong.
- Portfolio and network weigh as much as skills. Smith rates skills, portfolio and network as equal thirds, and has considered reversing the order. His line is "resumes talk, projects walk". After three or four rejections, he won a Utah Jazz internship with no interview by tagging the analytics staff on LinkedIn in a project scraping their shot data. For analysts, one SQL project and one data-viz project on a real interest is a start. He would avoid overused datasets (Titanic, Iris, MNIST, Google's bike-share capstone), and argues a bare GitHub is no portfolio when recruiters give it 10 seconds.
- Run the job hunt like an experiment. At about 14 hours a week Smith says a learner can be fully skill-ready in roughly 12 weeks, applying from week four to six; many of his students are hired in under 100 days. His analysis found about 15% of data jobs fully remote, against far more who want one. He has students A/B test résumés and keep a job tracker; one tracker showed 75% of applications going through LinkedIn Jobs, so he told her to switch platforms.