Operational Impacts

Building an In-House Candidate Schema: Essential Team Members, Costs, and Timelines

Andrew Buhrmann
April 13, 2023
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In our previous blog, we discussed the challenges staffing agencies face when attempting to build a universal candidate schema in-house. In this follow-up article, we will explore the types of people required to successfully build a candidate schema, as well as the estimated costs and timelines for implementation and maintenance.

Key Team Members for Building an In-House Candidate Schema:

Data Scientist:

A data scientist is essential for analyzing, structuring, and interpreting candidate data. They will be responsible for developing algorithms and machine learning models to identify patterns and relationships within the data, enabling better understanding and management of candidate skills.

Data Engineer:

A data engineer is responsible for designing, building, and maintaining the data infrastructure required to support the candidate schema. They will ensure seamless integration with existing systems, such as Applicant Tracking Systems (ATS), and optimize data storage and processing for efficient retrieval and analysis.

Domain Expert:

A domain expert with in-depth knowledge of the staffing industry and skill management is crucial for building a candidate schema that accurately reflects industry trends and evolving skill requirements. They will provide guidance on best practices and help the team understand the context of skills and their relevance to different job roles and industries.

Project Manager:

A project manager is responsible for overseeing the development and implementation of the candidate schema. They will coordinate the efforts of the team members, set timelines, and ensure that the project stays on track and within budget.

Estimated Costs and Timelines:

1. Implementation Costs:

The costs of building a candidate schema in-house will depend on several factors, including team size, expertise, and the complexity of the project. Some estimates for key team members include:

  • Data Scientist: $100,000 - $150,000 per year
  • Data Engineer: $90,000 - $130,000 per year
  • Domain Expert: $80,000 - $120,000 per year
  • Project Manager: $80,000 - $120,000 per year

Additionally, the costs for tools, resources, data processing, and enrichment can range from $50,000 to $100,000 during the implementation phase.

These figures represent base salaries and do not include additional costs such as benefits, taxes, and overhead.

2. Implementation Timeline:

The timeline for building a candidate schema in-house will vary depending on the complexity of the project and the resources available. On average, the implementation phase may take anywhere from 6 to 12 months. This includes data analysis, schema design, integration with existing systems, and testing.

3. Maintenance Costs and Timelines:

Maintaining a candidate schema requires continuous updates and adaptations to stay relevant and effective. The costs for maintaining a candidate schema will largely depend on the ongoing efforts of the team members and any additional resources required for monitoring industry trends and skill evolution. Maintenance costs can range from 20% to 50% of the initial implementation costs per year, which is approximately $50,000 to $250,000.

The total estimated costs for building and maintaining an in-house candidate schema can range from $450,000 to $850,000 for the first year and $50,000 to $250,000 per year for ongoing maintenance. Staffing agencies must carefully consider the team members, costs, and timelines involved in implementing and maintaining a candidate schema before deciding whether to pursue this approach. Alternatively, leveraging AI-driven talent management platforms, such as Candidate IQ™, can provide a more cost-effective and efficient solution for managing candidate skills and staying ahead in the competitive staffing industry.