How LTM Uses Snowflake Cortex AI to Predict Candidate Onboarding

LTM operates at the intersection of talent intensity and global scale—a technology consulting firm where the ability to recruit, onboard, and retain skilled professionals directly determines delivery capacity. With post-offer dropout rates reaching 30% in competitive hiring cycles, the gap between securing a candidate and having them show up on day one represents both operational and financial exposure. At LTM’s scale of over 80,000 users across HR and business operations, even marginal improvements in prediction accuracy translate into significant cost and productivity gains.

Before its migration to Snowflake, LTM’s HR analytics infrastructure relied on legacy on-premises systems characterized by data silos, high maintenance overhead, and slow query performance. Candidate onboarding predictions were delayed by weeks rather than days, and during peak hiring seasons, recruiting expenses ballooned by two to three times normal levels. The team lacked a unified view of historical hiring outcomes, making it impossible to identify candidates at risk of dropping out before those decisions became irreversible.

LTM executed a phased migration to Snowflake’s AI Data Cloud, beginning with a lift-and-shift of critical workloads using Snowflake’s Snowconvert tool, followed by modernization of ETL pipelines with zero-ETL connectors to SAP, SuccessFactors, and ATS platforms. The team then deployed machine learning models via Snowpark and integrated Snowflake Cortex AI to access large language models including Snowflake Arctic, Llama 3, and Mistral through a fully managed serverless environment. A joining probability predictor analyzes 12 predictive features from historical data, segmenting candidates into Red, Amber, and Green categories based on onboarding likelihood.

The most compelling outcome is the model’s 80% accuracy in predicting candidate onboarding 25 to 30 days before a start date—giving HR teams actionable lead time to implement personalized interventions like targeted offers or culture-fit sessions. Post-offer dropouts typically cost the organization one to three times the candidate’s salary; early identification converts that cost from a reactive write-off into a preventable outcome. The migration also delivered 10x faster query performance on critical hiring data and a 70% reduction in total cost of ownership.

LTM’s transformation positions talent acquisition as a data-driven, predictive function rather than a transactional process. With over 700 users across Talent Acquisition, Business, and Operations accessing insights via Power BI dashboards updated by automated daily model runs, the company has embedded AI into its standard hiring workflow. The broader implication is that large enterprises managing complex global talent pipelines can now treat onboarding probability as a measurable, improvable metric—shifting the conversation from “why did that candidate drop out?” to “what did we do to keep them?”