Data Analyst, Workforce Management Expert
New Jersey, United States
Evgenii Poremov
Council Member
Evgenii Poremov is a Data Analyst specializing in Workforce Management analytics, with a little over two years of professional experience in enterprise environments. They turn operational challenges into metrics, rules, and data structures that improve decision-making and reduce manual effort, and they own the SQL-based validation and control procedures that keep reports and dashboards reliable.
At VERME, where they worked as a Data Analyst from September 2023 to September 2025, they led the planning and improvement of employee work time processes using a Workforce Management product. They worked with cross-functional teams to identify operational inefficiencies and implement improvements, with the goal of raising operational efficiency and strengthening key business KPIs. Their work there centered on statistical analysis, database analysis, and the data structures behind workforce planning.
Alongside their analytics work, Evgenii Poremov has researched workforce scheduling and published three papers on the subject. Their paper on the architecture and information support of a universal workforce scheduling system for retail chains appeared in Modern Science-Intensive Technologies in May 2026. It examines a multi-level architecture, decomposes the scheduling module into business functions, services, and platform modules, and compares database architectures for planning and analytics tasks. A second paper, on data model design for an employee working time planning system, was published by the Eurasian Union of Scientists in March 2026. It covers the accounting of work shifts, user roles, regulatory constraints, and change history, and proposes a model that can be extended for analytical and managerial tasks. A third paper addresses the design of an information system for workforce scheduling in retail chains.
At VERME, where they worked as a Data Analyst from September 2023 to September 2025, they led the planning and improvement of employee work time processes using a Workforce Management product. They worked with cross-functional teams to identify operational inefficiencies and implement improvements, with the goal of raising operational efficiency and strengthening key business KPIs. Their work there centered on statistical analysis, database analysis, and the data structures behind workforce planning.
Alongside their analytics work, Evgenii Poremov has researched workforce scheduling and published three papers on the subject. Their paper on the architecture and information support of a universal workforce scheduling system for retail chains appeared in Modern Science-Intensive Technologies in May 2026. It examines a multi-level architecture, decomposes the scheduling module into business functions, services, and platform modules, and compares database architectures for planning and analytics tasks. A second paper, on data model design for an employee working time planning system, was published by the Eurasian Union of Scientists in March 2026. It covers the accounting of work shifts, user roles, regulatory constraints, and change history, and proposes a model that can be extended for analytical and managerial tasks. A third paper addresses the design of an information system for workforce scheduling in retail chains.
Their education includes a Bachelor's degree in Applied Mechanics from Bauman Moscow State Technical University (2019 to 2023), a Professional Retraining Diploma in Digital Marketing from the same university (2022 to 2023), and a Master's degree in Applied Computer Science from RTU MIREA (2023 to 2025).
Their technical expertise covers Workforce Management, database analysis, data modeling, SQL-driven data validation, statistical data analysis, and the building of reports and dashboards.
Their technical expertise covers Workforce Management, database analysis, data modeling, SQL-driven data validation, statistical data analysis, and the building of reports and dashboards.