Forecast: US job market poised for improvement in Q3, especially for certain experienced workers
The US job market is showing signs of stabilization after several years of uneven hiring and uncertainty, according to a new Q3 job forecast. Published in a from professional services company , the forecast projects conditions for several key areas of the U.S. labor market: all nonfarm job openings, professional services job openings, and information services (i.e., technology) job openings.
Because the company鈥檚 talent network of 30,000 professionals is fully distributed and focused on experienced workers, the report also includes a dedicated forecast for this segment of workers.
- All U.S. jobs: Bureau of Labor Statistics (BLS) data shows that overall job growth has declined at an average rate of 3.5% per quarter over the past several years. Toptal鈥檚 model suggests that trend will reverse in Q3, with the market showing slightly positive job growth.
- Professional services jobs: Job growth in professional services sectors like accounting, legal services, and marketing has also been declining by an average of 4% per quarter across the same time period, according to BLS data. The forecast suggests that the decline will level off during Q3, leaving job growth essentially flat.
- Information services (technology) jobs: Technology job growth has declined at an average rate of 5.5% per quarter in recent years, according to BLS data. The forecast suggests a notable shift from that pattern of contraction for Q3, projecting moderately positive job growth.
- Experienced remote and hybrid professional services and technology jobs: The BLS doesn鈥檛 track this segment of the market, so historical trend data for this group comes from the labor analytics firm Lightcast. That data shows that job growth for distributed professionals with five or more years of experience has increased by an average of 3.2% per quarter over the past several years. According to the forecast, that pace is expected to accelerate in Q3, with job growth rising more than the historical average.
Several factors may be contributing to the improving outlook. The report points to easing energy prices, relative resilience in the U.S economy, and the release of pent-up hiring demand after a period of elevated uncertainty.
In technology, employers are also gaining a more practical understanding of where AI delivers value and where human expertise remains essential, leading to renewed hiring as organizations refine their workforce strategies.
The Model Behind the Forecast
Toptal鈥檚 predictive model draws on 16 years of remote and hybrid job posting data from Lightcast, alongside several macroeconomic indicators that have historically tracked labor market performance.
One of those indicators is the , which captures both unemployed and underemployed workers. Unlike the more widely cited , the for economic reasons and those who have stopped actively searching for employment but still want work, providing a broader measure of labor market conditions.
The model also incorporates high-yield credit spreads, hiring rates, and inflation-adjusted GDP. Together, these indicators help capture shifts in business confidence, employer hiring behavior, and overall economic activity.
The resulting projections are then compared against several years of Toptal鈥檚 own client demand for experienced remote and hybrid professionals. According to the report, distributed hiring often serves as an early signal of broader labor market movements, because companies frequently use remote talent to address immediate needs or launch new initiatives before expanding to permanent hiring.
The report cautions that the projections should be viewed as directional rather than definitive. Although the model accounts for historical labor market relationships and current economic conditions, the U.S. job market remains uncertain, and hiring outcomes will continue to be influenced by factors not captured in the analysis. It also notes that while remote and hybrid hiring patterns have shown a strong correlation with broader market movements, additional research is needed to better understand the relationship between those trends.
Methodology
The forecast uses an ordered logistic regression model, which takes a set of input variables and uses historical data to estimate the relationship between those inputs and a particular outcome. The model鈥檚 directional projections are then compared against Toptal鈥檚 own client demand data for remote and hybrid professionals, providing a cross-check between modeled signals and observed market activity. Inputs include 16 years of remote and hybrid job posting data from Lightcast, the U-6 unemployment rate, high-yield credit spreads, hiring rates, and inflation-adjusted GDP.
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