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I built a machine learning model to predict who leaves tech jobs early. The results surprised me.

Posted on May 11, 2026 By 164news66 No Comments on I built a machine learning model to predict who leaves tech jobs early. The results surprised me.

Why Early Attrition in Tech Is More About Career Momentum Than Culture

May 11, 2026 – 8:41 pm

Image by: Kwan Chun Clinton, Ngan

TL;DR

A People Analytics study analyzing 205 tech professionals found that early employee attrition is driven more by stalled career momentum than workplace culture. Promotions, internal mobility, and visible growth opportunities were the strongest predictors of retention, while team socialization had little measurable impact.

I went into this research convinced I already knew the answer.

After more than a decade in People Analytics, the last few years at Meta, I had a working theory about why tech employees leave their jobs within the first year. Two things, I believed, were doing most of the damage: whether someone was getting promoted, and how often they were socialising with their immediate team outside of work. The first felt obvious. The second felt like the kind of human factor the industry consistently underweights.

I was half right.

When I surveyed 205 tech professionals globally and trained a machine learning model to predict early attrition, promotions came out as the single strongest signal in the dataset. But socialisation? It barely registered. And the factors that did matter alongside promotions pointed somewhere I hadn’t fully anticipated. Early attrition in tech isn’t primarily a culture problem. It’s a career momentum problem.

Tech’s Attrition Dilemma

The technology industry has one of the highest attrition rates of any sector. Median tenure at many tech companies sits at around one year, regardless of company size. This isn’t a post-pandemic hangover or a hot job market anomaly. It’s been the structural baseline for as long as the industry has existed, and the industry has never really solved it.

The costs are well documented. Replacing an employee can run up to 2.5 times their salary once you factor in recruiting, onboarding, lost productivity, and the institutional knowledge that walks out the door with them. Research suggests that a single standard deviation increase in attrition rate correlates with an 8.9% drop in profits. In an era where tech companies are simultaneously pouring billions into AI infrastructure and scrutinising every other line of their cost base, haemorrhaging money on preventable attrition is a harder position to defend than it used to be.

Unaddressed Root Causes

What’s less well understood is why the problem persists despite enormous investment in trying to fix it. Tech companies spend heavily on perks, engagement programs, culture initiatives, and manager training. Some of it works at the margins. None of it has bent the curve in any meaningful way.

Part of the reason, I’d argue, is that most retention efforts are reactive. Someone signals they’re unhappy, or worse, hands in their notice, and the response kicks in. By then it’s usually too late. The question that has always interested me professionally isn’t how to respond to attrition once it’s happened but how to prevent it from happening in the first place.

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