PhD Sergei Markochev

About the Expert

Sergei Markochev is Lead Data Scientist at Phaidon International, a global recruitment company. He holds a PhD in nuclear physics from the Moscow Institute of Physics and Technology and has over a decade of experience in data science, working across industries such as green energy, media, and investment consulting in London.

At Phaidon, Sergei applies machine learning and large language models (LLMs) to improve candidate-job matching, build predictive models for business forecasting, and develop real-time business intelligence tools for recruitment consultants.

What happens when AI helps you find the right people — before you even start looking?

Artificial intelligence is quietly transforming one of the oldest industries in the world — recruitment. In this insightful conversation, Sergei Markochev, Lead Data Scientist at Phaidon International, one of the world's largest recruitment agencies, explains how AI is changing the way companies find and assess talent.

From predictive analytics to AI-powered candidate screening, Sergei shares how modern recruitment teams use data science not only to fill jobs faster, but to predict market trends and improve the performance of recruiters themselves.

How AI transforms recruitment from manual work to smart insights

Recruitment is still largely built on CVs and interviews, especially in the technology and finance sectors where Phaidon operates. But Sergei explains that AI in recruitment is rapidly changing one key part of the process: candidate screening.

AI-powered algorithms now help match CVs to job descriptions, quickly suggesting qualified candidates. But Sergei emphasizes: the final decision remains human. Recruiters still speak with candidates, assess cultural fit, and build personal relationships — parts of the process that AI can't replace.

Beyond automation, Sergei and his team use predictive analytics to forecast market activity months in advance, helping recruitment teams understand hiring trends before they happen.

What's next for AI in talent acquisition?

Sergei believes the next challenge isn't the technology — it's the data quality. Recruitment data is messy, often full of human errors, missing steps, or inconsistent updates. Without clean data, no AI system can deliver accurate predictions.

He also points out that personalized AI assessments — like real-time knowledge tests — already exist, but standardization remains critical. When companies assess candidates, they need comparable results, not completely different questions for every person.

Sergei argues that AI education and online learning platforms play a key role in preparing employees to work with AI tools in recruitment, predictive analytics, and cybersecurity. Regular upskilling, clear use cases, and real-world practice are essential for future-proof talent.