Is the Job Market Dying?
Fewer openings, aggressive filtering tools, and widespread “shotgun” applications are making tech jobs much harder to land, especially for juniors and data scientists, even as some seniors still see opportunities through networks and niche roles. Commenters describe a market more like a post-bubble hangover than a collapse: churn is down, companies are optimizing for fewer but more productive hires, and many have abandoned public job postings in favor of referrals and direct outreach. Hiring processes themselves — from AI résumé screens to automated video interviews and culture‐fit theatrics — are widely seen as dehumanizing and inefficient, prompting calls to rely more on personal networks, government or “boring” corporate jobs, and realistic expectations about pay and role prestige.
Overall state of the tech job market
- Many describe the market as “shitty but not dead” – worse than 2009/2020, not quite dot‑com nuclear winter, roughly comparable to 2008.
- Strong sense of contraction and “musical chairs”: fewer openings, especially speculative/ZIRP-era roles; far more applicants per role.
- Disagreement on who is hit hardest: some say seniors with strong skills are still fine; others say even very senior people are struggling for a year+.
- Juniors and new grads are widely seen as in the worst position.
Reduced churn and changing company behavior
- Several note much lower employee movement. Less churn means more efficiency per engineer and fewer openings.
- Hiring now needs to justify profitability, not just growth; higher rates and capital costs make headcount harder to approve.
- Some companies stop public postings, hiring mostly via referrals, direct outreach, or their careers page, aiming for “good enough” rather than “the best.”
Application volume, filters, and AI
- Applying to hundreds or thousands of roles is common; some report ~100:10:1 application‑to‑offer ratios over decades.
- This volume pushes companies toward crude filters (ATS keywords, simple heuristics, possibly AI) that randomly exclude many qualified people.
- Debate: some argue mass‑applying is rational in this environment; others say repeated rejection signals a need to fix resumes/strategy.
Dehumanizing hiring practices
- Widespread dislike of one‑way recorded video interviews and AI‑scored interviews; described as dystopian and dehumanizing.
- Complaints about gimmicky “show your personality” videos and humiliating icebreakers.
- Concern that hiring that dehumanizes candidates signals bad work cultures.
Role‑specific dynamics (esp. Data Science)
- Many view “data scientist” as overhired during the boom; teams are shrinking and skewing toward engineers and analysts.
- Data science compared to “HTML programmer” in the dot‑com era: commoditized, flooded, and often low quality, making hiring riskier.
- Advice from some: pivot to business analyst or other roles with clearer business value.
Coping strategies and alternatives
- Emphasis on networks and referrals, but also recognition that networking isn’t equally effective for everyone.
- Suggested fallbacks: government/defense or state IT, local non‑FAANG companies, contracting, non‑tech jobs while staying “in the game,” or entrepreneurship/side projects.
- Ongoing debate about “hustle” and working extra hours: some see it as necessary risk‑reward; others see exploitation and burnout.