How LTM Uses Snowflake Cortex AI to Predict Candidate Onboarding
LTM is a global technology consulting and digital solutions company operating at scale across highly competitive talent markets. The company deployed Snowflake’s AI Data Cloud, Cortex AI, and Snowpark to unify fragmented HR data and build a machine learning model that predicts candidate onboarding probability 25–30 days before a start date. The result: an 80% prediction accuracy rate, 70% reduction in total cost of ownership, and a hiring process transformed from reactive to proactive.
Impact
70%
Total cost of ownership reduction
10x faster
Query performance improvement
80%
Candidate onboarding prediction accuracy
5x faster
AI/ML application development speed
700+
Users accessing AI-driven hiring insights
Challenge
LTM’s HR analytics ran on legacy on-premises systems with data silos and slow processing, causing candidate onboarding predictions to lag by weeks and driving recruiting costs two to three times higher during peak hiring seasons, with post-offer dropout rates as high as 30%.
Solution
LTM migrated to Snowflake’s AI Data Cloud and deployed Snowpark-based ML models alongside Snowflake Cortex AI to build a joining probability predictor that segments candidates by onboarding risk 25–30 days before their start date, enabling targeted retention interventions.
Tools & Technologies
What Leaders Say
“The hiring landscape is evolving rapidly, and technology is no longer just an enabler — it is a strategic partner in shaping better candidate experiences and driving measurable outcomes.”
“By leveraging Snowflake as our data platform, we’ve achieved seamless alignment between technology and business, enabling advanced models like joiner prediction with exceptional accuracy. This milestone is a testament to how innovation and technology converge to redefine and transform recruitment.”
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Full Story
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?”
AWS